RESEARCH CENTRE

Inria Centre
at Rennes University

IN PARTNERSHIP WITH:

Institut national des sciences appliquées
de Rennes, CNRS, École normale
supérieure de Rennes, Université de
Rennes

2023
ACTIVITY REPORT

Project-Team

MYRIADS

Design and Implementation of
Autonomous Distributed Systems

IN COLLABORATION WITH: Institut de recherche en informatique et
systèmes aléatoires (IRISA)

DOMAIN

Networks, Systems and Services,
Distributed Computing

THEME

Distributed Systems and middleware

Contents

Project-Team MYRIADS

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 General Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

3

4
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5

3 Research program

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Scaling fogs and clouds
3.2.1 Resource management in hierarchical clouds . . . . . . . . . . . . . . . . . . . . . . .
3.2.2 Resource management in fog computing architectures . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . .
3.2.3 Self-optimizing applications in multi-cloud environments
3.3 Greening clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.1 Smart grids and clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.2 Energy cost models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.3 Energy-aware users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Securing clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 Security monitoring service level objectives . . . . . . . . . . . . . . . . . . . . . . . .
3.4.2 Data protection in Cloud-based IoT services . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Experimenting with Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . .

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3.5.1 Experimentation methodologies for clouds
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3.5.2 Use cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4 Application domains

10
4.1 Main application domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

5 Social and environmental responsibility

11
5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

6 Highlights of the year

11
6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

7 New software, platforms, open data

11
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.1 SimGrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.2 Tansiv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7.1.3 EnOSlib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

8 New results

13
8.1 Scaling Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.1.1 Fog computing platform design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.1.2 Advanced data management in shared environments
. . . . . . . . . . . . . . . . . . 14
8.1.3 Geo-distributed graph data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.1.4 Resource Allocation for Serverless ML Workflows . . . . . . . . . . . . . . . . . . . . . 15
8.1.5 Geo-distributed data stream processing . . . . . . . . . . . . . . . . . . . . . . . . . . 15
8.1.6 Fault tolerance for Function-as-a-Service environments . . . . . . . . . . . . . . . . . 15
8.1.7 Flexible function placement for Function-as-a-Service in the fog . . . . . . . . . . . . 16
8.1.8 Balancing performance and sustainability for Function-as-a-Service in the fog . . . 16
8.1.9 Reliable fog platforms in adverse natural environments . . . . . . . . . . . . . . . . . 16
8.1.10 Modeling cloud infrastructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

8.2 Greening Clouds

8.2.1 Energy Models for Cloud infrastructures . . . . . . . . . . . . . . . . . . . . . . . . . . 17
8.2.2 End-to-end energy models for the Internet of Things . . . . . . . . . . . . . . . . . . . 17
8.2.3 Exploiting renewable energy in distributed clouds . . . . . . . . . . . . . . . . . . . . 18
8.2.4 Smart Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
8.2.5 End-to-end ecodesign of cloud platforms . . . . . . . . . . . . . . . . . . . . . . . . . . 18
8.3 Experimenting with Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.3.1 Simulating distributed IT systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.3.2 Toward stealth analysis of distributed applications . . . . . . . . . . . . . . . . . . . . 19
8.3.3 Tools for experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

9 Bilateral contracts and grants with industry

20
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

10 Partnerships and cooperations

21
10.1 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.2 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

11 Dissemination

23
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
11.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 26
11.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
11.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
11.3.1 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 29
11.3.2 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
11.3.3 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

12 Scientific production

29
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
12.3 Cited publications

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Project-Team MYRIADS

Creation of the Project-Team: 2012 January 01

Keywords

Computer sciences and digital sciences

A1.1.9. – Fault tolerant systems

A1.1.13. – Virtualization

A1.2. – Networks

A1.2.4. – QoS, performance evaluation

A1.2.5. – Internet of things

A1.3. – Distributed Systems

A1.3.2. – Mobile distributed systems

A1.3.4. – Peer to peer

A1.3.5. – Cloud

A1.3.6. – Fog, Edge

A1.6. – Green Computing

A2.1.7. – Distributed programming

A2.2.5. – Run-time systems

A2.3.2. – Cyber-physical systems

A2.4.2. – Model-checking

A2.6. – Infrastructure software

A2.6.1. – Operating systems

A2.6.2. – Middleware

A2.6.3. – Virtual machines

A2.6.4. – Ressource management

A3.1.3. – Distributed data

A4.9. – Security supervision

A4.9.1. – Intrusion detection

A4.9.3. – Reaction to attacks

A7.1. – Algorithms

A8.2. – Optimization

Other research topics and application domains

B2.3. – Epidemiology

B3.1. – Sustainable development

B3.2. – Climate and meteorology

B4.3. – Renewable energy production

B4.4. – Energy delivery

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B4.4.1. – Smart grids

B4.5. – Energy consumption

B4.5.1. – Green computing

B5.1. – Factory of the future

B5.8. – Learning and training

B6.1. – Software industry

B6.1.1. – Software engineering

B6.3. – Network functions

B6.3.3. – Network Management

B6.4. – Internet of things

B6.5. – Information systems

B6.6. – Embedded systems

B8.1. – Smart building/home

B8.2. – Connected city

B8.3. – Urbanism and urban planning

B8.5. – Smart society

B9.1. – Education

B9.1.1. – E-learning, MOOC

B9.1.2. – Serious games

B9.5.1. – Computer science

B9.7. – Knowledge dissemination

B9.7.1. – Open access

B9.7.2. – Open data

B9.8. – Reproducibility

B9.9. – Ethics

B9.10. – Privacy

Project MYRIADS

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1 Team members, visitors, external collaborators

Research Scientists

• Shadi Ibrahim [INRIA, Researcher]

• Anne-Cécile Orgerie [CNRS, Senior Researcher, HDR]

Faculty Members

• Guillaume Pierre [Team leader, UNIV RENNES, Professor]

• Marin Bertier [INSA RENNES, Associate Professor]

• François Lemercier [UNIV RENNES, Associate Professor]

• Nikolaos Parlavantzas [INSA RENNES, Associate Professor, HDR]

• Jean-Louis Pazat [INSA RENNES, Professor, HDR]

• Martin Quinson [ENS RENNES, Professor, HDR]

• Cédric Tedeschi [UNIV RENNES, Professor, HDR]

Post-Doctoral Fellow

• Alessio Pagliari [UNIV RENNES]

PhD Students

• Khaled Arsalane [UNIV RENNES]

• Leo Cosseron [ENS RENNES]

• Clément Courageux-Sudan [ENS RENNES]

• Wedan Gnibga [CNRS]

• Chih-Kai Huang [UNIV RENNES]

• Ammar Kazem [UNIV RENNES, from Oct 2023]

• Govind Kovilkkatt Panickerveetil [INRIA, from Mar 2023]

• Mohamed Cherif Zouaoui Latreche [INSA RENNES, from Oct 2023]

• Mathieu Laurent [ENS RENNES, from Sep 2023]

• Volodia Parol–Guarino [INRIA]

• Mohammad Rizk [INRIA, from Nov 2023]

• Matthieu Silard [CNRS, from Feb 2023]

Technical Staff

• Adrien Gougeon [ENS RENNES]

• Mathieu Simonin [INRIA, Engineer]

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Inria Annual Report 2023

Interns and Apprentices

• Hugo Depuydt [ENS Rennes, Intern]

• Weissem Trabelsi [UNIV RENNES, Intern]

Administrative Assistants

• Stephanie Gosselin Lemaile [INRIA]

• Angélique Jarnoux [INRIA]

Visiting Scientist

• Maxwell Pirtle [Northeastern University]

External Collaborators

• Anne Blavette [CNRS]

• Louis Rilling [DGA, until Nov 2023]

2 Overall objectives

2.1 General Objectives

MYRIADS is a joint team with INRIA, CNRS, UNIVERSITY RENNES 1, INSA RENNES and ENS RENNES. It is
part of IRISA (D1 department on large scale systems) and INRIA RENNES – BRETAGNE ATLANTIQUE.

The objective of MYRIADS is to design and implement systems for autonomous service and resource
management in interconnected and distributed clouds. The team tackles the challenges of dependable
application execution and efficient resource management in highly distributed clouds.

2.2 Context

The MYRIADS team research activities are conducted in the context of the future of Internet.

Internet of Services. Myriads of applications are provided to more than one billion users 1 all over
the world. Over time, these applications are becoming more and more sophisticated, a given
application being a composition of services likely to be executed on various sites located in different
geographical locations. The Internet of Services is spreading all domains: home, administration,
business, industry and science. Everyone is involved in the Internet of Services: citizens, enterprises,
scientists are application, service and resource consumers and/or providers over the Internet.

Outsourcing. Software is provided as a service over the Internet. Myriads of applications are available on-
line to billions of users as, for instance, GoogleApps (Gmail). After decades in which companies used
to host their entire IT infrastructures in-house, a major shift is occurring where these infrastructures
are outsourced to external operators such as Data Centers and Computing Clouds. In the Internet
of Services, not only software but also infrastructure are delivered as a service. Clouds turned
computing and storage into a utility. Just like water or electricity, they are available in virtually
infinite amounts and their consumption can be adapted within seconds like opening or closing
a water tap. The main transition, however, is the change in business models. Companies or
scientists do not need to buy and operate their own data centers anymore. Instead, the compute
and storage resources are offered by companies on a “pay-as-you-go” basis. There is no more need
for large hardware investments before starting a business. Even more, the new model allows users
to adapt their resources within minutes, e.g., scale up to handle peak loads or rent large numbers

1According to World Stats, there are 4.94 billion Internet users i.e., more than 60% of the total world population in December

2020.

Project MYRIADS

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of computers for a short experiment. The risk of wasting money by either under-utilization or
undersized data centers is shifted from the user to the provider.

Sharing and Cooperation. Sharing information and cooperating over the Internet are also important
user needs both in the private and the professional spheres. This is exemplified by various services
that have been developed in the last decade. Peer-to-peer networks are extensively used by citizens
in order to share musics and movies. A service like Flickr allowing individuals to share pictures is
also very popular. Social networks such as FaceBook or Linkedln link millions of users who share
various kinds of information within communities. Virtual organizations tightly connected to Grids
allow scientists to share computing resources aggregated from different institutions (universities,
computing centers...). The EGEE European Grid is an example of production Grid shared by
thousands of scientists all over Europe.

2.3 Challenges

The term cloud was coined 18 years ago. Today cloud computing is widely adopted for a wide range
of usage: information systems outsourcing, web service hosting, scientific computing, data analytics,
back-end of mobile and IoT applications. There is a wide variety of cloud service providers (IaaS, PaaS,
SaaS) resulting in difficulties for customers to select the services fitting their needs. Production clouds
are powered by huge data centers that customers reach through the Internet. This current model raises
a number of issues. Cloud computing generates a lot of traffic resulting in ISP providers needing to
increase the network capacity. An increasing amount of always larger data centers consumes a lot of
energy. Cloud customers experience poor quality of experience for highly interactive mobile applications
as their requests are dealt with in data centers that are several hops away. The centralization of data in
clouds also raises (i) security issues as clouds are a target of choice for attackers and (ii) privacy issues
with data aggregation.

Recently new cloud architectures have been proposed to overcome the scalability, latency, and energy
issues of traditional centralized data centers. Various flavors of distributed cloud computing are emerging
depending on the resources exploited: resources in the core network (distributed cloud), resources at the
edge of the network (edge clouds) and even resources in the swarms of people’s devices (fog computing)
enabling scalable cloud computing. These distributed clouds raise new challenges for resource and
application management.

The ultimate goal of the Myriads team is making highly distributed clouds sustainable. By sustainab-
ility we mean green, efficient and secure clouds. We plan to study highly distributed clouds including
edge clouds and fog computing. In this context, we will investigate novel techniques for greening clouds
including the optimization of energy consumption in distributed clouds in the context of smart grids.
As more and more critical information systems are outsourced in the cloud and personal data captured
by sensors embedded in smart objects and smartphones are stored in the cloud, we will investigate
security and privacy issues in two directions: cloud security monitoring and personal data protection in
cloud-based IoT applications.

System research requires experimental validation based on simulation and/or prototyping. Reprodu-
cible experimentation is essential. We will contribute to the design and implementation of simulators
well suited to the study of distributed clouds (architecture, energy consumption) and of large scale
experimentation platforms for distributed systems enabling reproducible experiments.

3 Research program

3.1 Introduction

In this section, we present our research challenges along four work directions: resource and application
management in distributed cloud and fog computing architectures for scaling clouds in Section 3.2,
energy management strategies for greening clouds in Section 3.3, security and data protection aspects for
securing cloud-based information systems and applications in Section 3.4, and methods for experiment-
ing with clouds in Section 3.5.

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3.2 Scaling fogs and clouds

3.2.1 Resource management in hierarchical clouds

The next generation of utility computing appears to be an evolution from highly centralized clouds
towards more decentralized platforms. Today, cloud computing platforms mostly rely on large data
centers servicing a multitude of clients from the edge of the Internet. Servicing cloud clients in this
manner suggests that locality patterns are ignored: wherever the client issues his/her request from, the
request will have to go through the backbone of the Internet provider to the other side of the network
where the data center relies. Besides this extra network traffic and this latency overhead that could
be avoided, other common centralization drawbacks in this context include limitations in terms of
security/legal issues and resilience.

At the same time, it appears that network backbones are over-provisioned for most of their usage.
This may advocate for placing computing resources directly within the backbone network. The general
challenge of resource management for such clouds stands in trying to be locality-aware: for the needs of
an application, several virtual machines may exchange data. Placing them close to each other can signi-
ficantly improve the performance of the application they compose. More generally, building an overlay
network that takes into account the hierarchical aspects of the platform without being a hierarchical
overlay – which comes with load balancing and resilience issues – is a challenge by itself.

3.2.2 Resource management in fog computing architectures

Fog computing infrastructures are composed of compute, storage and networking resources located at
the edge of wide-area networks, in immediate proximity to the end users. Instead of treating the mobile
operator’s network as a high-latency dumb pipe between the end users and the external service providers,
fog platforms aim at deploying cloud functionalities within the mobile phone network, inside or close
to the mobile access points. Doing so is expected to deliver added value to the content providers and
the end users by enabling new types of applications ranging from Internet-of-Things applications to
extremely interactive systems (e.g., augmented reality). Simultaneously, it will generate extra revenue
streams for the mobile network operators, by allowing them to position themselves as cloud computing
operators and to rent their already-deployed infrastructure to content and application providers.

Fog computing platforms have a very different geographical distribution compared to traditional
clouds. While traditional clouds are composed of many reliable and powerful machines located in
a very small number of data centers and interconnected by very high-speed networks, mobile edge
cloud are composed of a very large number of points-of-presence with a couple of weak and potentially
unreliable servers, interconnected with each other by commodity long-distance networks. This creates
new demands for the organization of a scalable mobile edge computing infrastructure, and opens new
directions for research.

The main challenges that we plan to address are:

• How should an edge cloud infrastructure be designed such that it remains scalable, fault-tolerant,

controllable, energy-efficient, etc.?

• How should applications making use of edge clouds be organized? One promising direction is to
explore the extent to which stream-data processing platforms such as Apache Spark and Apache
Flink can be adapted to become one of the main application programming paradigms in such
environments.

• How data should be stored and managed to facilitate the deployment of Fog infrastructures and

IoT applications while taking into acccount the limited storage capacity.

3.2.3 Self-optimizing applications in multi-cloud environments

As the use of cloud computing becomes pervasive, the ability to deploy an application on a multi-cloud
infrastructure becomes increasingly important. Potential benefits include avoiding dependence on a
single vendor, taking advantage of lower resource prices or resource proximity, and enhancing application
availability. Supporting multi-cloud application management involves two tasks. First, it involves

Project MYRIADS

7

selecting an initial multi-cloud application deployment that best satisfies application objectives and
optimizes performance and cost. Second, it involves dynamically adapting the application deployment
in order to react to changes in execution conditions, application objectives, cloud provider offerings, or
resource prices. Handling price changes in particular is becoming increasingly complex. The reason is
the growing trend of providers offering sophisticated, dynamic pricing models that allow buying and
selling resources of finer granularities for shorter time durations with varying prices.

Although multi-cloud platforms are starting to emerge, these platforms impose a considerable amount
of effort on developers and operations engineers, provide no support for dynamic pricing, and lack the
responsiveness and scalability necessary for handling highly-distributed, dynamic applications with strict
quality requirements. The goal of this work is to develop techniques and mechanisms for automating
application management, enabling applications to cope with and take advantage of the dynamic, diverse,
multi-cloud environment in which they operate.

The main challenges arising in this context are:

• selecting effective decision-making approaches for application adaptation,

• supporting scalable monitoring and adaptation across multiple clouds,

• performing adaptation actions in a cost-efficient and safe manner.

3.3 Greening clouds

The ICT (Information and Communications Technologies) ecosystem now approaches 5% of world
electricity consumption and this ICT energy use will continue to grow fast because of the information
appetite of Big Data, large networks and large infrastructures as Clouds that unavoidably leads to large
power.

3.3.1 Smart grids and clouds

We propose exploiting Smart Grid technologies to come to the rescue of energy-hungry Clouds. Unlike
in traditional electrical distribution networks, where power can only be moved and scheduled in very
limited ways, Smart Grids dynamically and effectively adapt supply to demand and limit electricity losses
(currently 10% of produced energy is lost during transmission and distribution).

For instance, when a user submits a Cloud request (such as a Google search for instance), this is
routed to a data center that processes it, computes the answer, and sends it back to the user. Google
owns several data centers spread across the world and for performance reasons, the center answering the
user’s request is more likely to be the one closest to the user. However, this data center may be less energy
efficient. The request may have consumed less energy, or a different kind of energy (renewable or not), if
it had been sent to a more distant data center. In this case, the response time would have been increased
but maybe not noticeably: a different trade-off between quality of service (QoS) and energy-efficiency
could have been adopted.

While Clouds come naturally to the rescue of Smart Grids for dealing with this big data issue, little
attention has been paid to the benefits that Smart Grids could bring to distributed Clouds. To our
knowledge, no previous work has exploited the Smart Grids potential to obtain and control the energy
consumption of entire Cloud infrastructures from underlying facilities such as air conditioning equipment
(which accounts for 30% to 50% of a data center’s electricity bill) to network resources (which are often
operated by several actors) and to computing resources (with their heterogeneity and distribution across
multiple data centers). We aim at taking advantage of the opportunity brought by the Smart Grids to
exploit renewable energy availability and to optimize energy management in distributed Clouds.

3.3.2 Energy cost models

Cloud computing allows users to outsource the computer resources required for their applications
instead of using a local installation. It offers on-demand access to the resources through the Internet
with a pay-as-you-go pricing model. However, this model hides the electricity cost of running these
infrastructures.

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Inria Annual Report 2023

The costs of current data centers are mostly driven by their energy consumption (specifically by the
air conditioning, computing and networking infrastructures). Yet, current pricing models are usually
static and rarely consider the facilities’ energy consumption per user. The challenge is to provide a
fair and predictable model to attribute the overall energy costs per virtual machine and to increase
energy-awareness of users.

Another goal consists in better understanding the energy consumption of computing and network-
ing resources of Clouds in order to provide energy cost models for the entire infrastructure including
incentivizing cost models for both Cloud providers and energy suppliers. These models will be based on
experimental measurement campaigns on heterogeneous devices. Inferring a cost model from energy
measurements is an arduous task since simple models are not convincing, as shown in our previous work.
We aim at proposing and validating energy cost models for the heterogeneous Cloud infrastructures
in one hand, and the energy distribution grid on the other hand. These models will be integrated into
simulation frameworks in order to validate our energy-efficient algorithms at larger scale.

3.3.3 Energy-aware users

In a moderately loaded Cloud, some servers may be turned off when not used for energy saving purpose.
Cloud providers can apply resource management strategies to favor idle servers. Some of the existing
solutions propose mechanisms to optimize VM scheduling in the Cloud. A common solution is to
consolidate the mapping of the VMs in the Cloud by grouping them in a fewer number of servers. The
unused servers can then be turned off in order to lower the global electricity consumption.

Indeed, current work focuses on possible levers at the virtual machine suppliers and/or services.
However, users are not involved in the choice of using these levers while significant energy savings could
be achieved with their help. For example, they might agree to delay slightly the calculation of the response
to their applications on the Cloud or accept that it is supported by a remote data center, to save energy or
wait for the availability of renewable energy. The VMs are black boxes from the Cloud provider point of
view. So, the user is the only one to know the applications running on her VMs.

We plan to explore possible collaborations between virtual machine suppliers, service providers
and users of Clouds in order to provide users with ways of participating in the reduction of the Clouds
energy consumption. This work will follow two directions: 1) to investigate compromises between power
and performance/service quality that cloud providers can offer to their users and to propose them a
variety of options adapted to their workload; and 2) to develop mechanisms for each layer of the Cloud
software stack to provide users with a quantification of the energy consumed by each of their options as
an incentive to become greener.

3.4 Securing clouds

3.4.1 Security monitoring service level objectives

While the trend for companies to outsource their information system in clouds is confirmed, the problem
of securing an information system becomes more difficult. Indeed, in the case of infrastructure clouds,
physical resources are shared between companies (also called tenants) but each tenant controls only
parts of the shared resources, and, thanks to virtualization, the information system can be dynamically
and automatically reconfigured with added or removed resources (for example starting or stopping virtual
machines), or even moved between physical resources (for example using virtual machine migration).
Partial control of shared resources brings new classes of attacks between tenants, and security monitoring
mechanisms to detect such attacks are better placed out of the tenant-controlled virtual information
systems, that is under control of the cloud provider. Dynamic and automatic reconfigurations of the
information system make it unfeasible for a tenant’s security administrator to setup the security monitor-
ing components to detect attacks, and thus an automated self-adaptable security monitoring service is
required.

Combining the two previous statements, there is a need for a dependable, automatic security monit-
oring service provided to tenants by the cloud provider. Our goal is to address the following challenges to
design such a security monitoring service:

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1. to define relevant Service-Level Objectives (SLOs) of a security monitoring service, that can figure

in the Service-Level Agreement (SLA) signed between a cloud provider and a tenant;

2. to design heuristics to automatically configure provider-controlled security monitoring software
components and devices so that SLOs are reached, even during automatic reconfigurations of
tenants’ information systems;

3. to design evaluation methods for tenants to check that SLOs are reached.

Moreover in challenges 2 and 3 the following sub-challenges must be addressed:

• although SLAs are bi-lateral contracts between the provider and each tenant, the implementation
of the contracts is based on shared resources, and thus we must study methods to combine the
SLOs;

• the designed methods should have a minimal impact on performance.

3.4.2 Data protection in Cloud-based IoT services

The Internet of Things is becoming a reality. Individuals have their own swarm of connected devices
(e.g. smartphone, wearables, and home connected objects) continually collecting personal data. A novel
generation of services is emerging exploiting data streams produced by the devices’ sensors. People
are deprived of control of their personal data as they don’t know precisely what data are collected by
service providers operating on Internet (oISPs), for which purpose they could be used, for how long they
are stored, and to whom they are disclosed. In response to privacy concerns the European Union has
introduced, with the Global Data Protection Regulation (GDPR), new rules aimed at enforcing the people’s
rights to personal data protection. The GDPR also gives strong incentives to oISPs to comply. However,
today, oISPs can’t make their systems GDPR-compliant since they don’t have the required technologies.
We argue that a new generation of system is mandatory for enabling oISPs to conform to the GDPR.
We plan to to design an open source distributed operating system for native implementation of new
GDPR rules and ease the programming of compliant cloud-based IoT services. Among the new rules,
transparency, right of erasure, and accountability are the most challenging ones to be implemented in IoT
environments but could fundamentally increase people’s confidence in oISPs. Deployed on individuals’
swarms of devices and oISPs’ cloud-hosted servers, it will enforce detailed data protection agreements
and accountability of oISPs’ data processing activities. Ultimately we will show to what extend the new
GDPR rules can be implemented for cloud-based IoT services. In addition, we are also working on new
approaches to allow the running of graph applications in geo-distributed Clouds while respecting the
data protection regulations in different locations.

3.5 Experimenting with Clouds

Cloud platforms are challenging to evaluate and study with a sound scientific methodology. As with any
distributed platform, it is very difficult to gather a global and precise view of the system state. Experiments
are not reproducible by default since these systems are shared between several stakeholders. This is even
worsened by the fact that microscopic differences in the experimental conditions can lead to drastic
changes since typical Cloud applications continuously adapt their behavior to the system conditions.

3.5.1 Experimentation methodologies for clouds

We propose to combine two complementary experimental approaches: direct execution on testbeds
such as Grid’5000, that is eminently convincing but rather labor intensive, and simulations (using e.g.,
SimGrid) that are much more light-weight, but require careful assessments. One specificity of the Myriads
team is that we are working on these experimental methodologies per se, raising the standards of good
experiments in our community.

We plan to make SimGrid widely usable beyond research laboratories, in order to evaluate industrial
systems and to teach the future generations of cloud practitioners. This requires to frame the specific
concepts of Cloud systems and platforms in actionable interfaces. The challenge is to make the framework

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both easy to use for simple studies in educational settings while modular and extensible to suit the specific
needs of advanced industrial-class users.

We aim at leveraging the convergence opportunities between methodologies by further bridging
simulation and real testbeds. The predictions obtained from the simulator should be validated against
some real-world experiments obtained on the target production platform, or on a similar platform. This
(in)validation of the predicted results often improves the understanding of the modeled system. On the
other side, it may even happen that the measured discrepancies are due to some mis-configuration of
the real platform that would have been undetected without this (in)validation study. In that sense, the
simulator constitutes a precious tool for the quality assurance of real testbeds such as Grid’5000.

Scientists need more help to make their Cloud experiments fully reproducible, in the sprit of Open
Science exemplified by the HAL Open Archive, actively backed by Inria. Users still need practical solutions
to archive, share and compare the whole experimental settings, including the raw data production
(particularly in the case of real testbeds) and their statistical analysis. This is a long lasting task to which
we plan to collaborate through the research communities gathered around the Grid’5000 and SimGrid
scientific instruments.

Finally, since correction and performance can constitute contradictory goals, it is particularly import-
ant to study them jointly. To that extend, we want to bridge the performance studies, that constitute our
main scientific heritage, to correction studies leveraging formal techniques. SimGrid already includes
support to exhaustively explore the possible executions. We plan to continue this work to ease the use of
the relevant formal methods to the experimenter studying Cloud systems.

3.5.2 Use cases

In system research, it is important to work on real-world use cases from which we extract requirements
inspiring new research directions and with which we can validate the system services and mechanisms we
propose. In the framework of our close collaboration with the Data Science Technology department of the
Lawrence Berkeley National Lab (LBNL), we will investigate cloud usage for scientific data management.
Next-generation scientific discoveries are at the boundaries of datasets, e.g., across multiple science
disciplines, institutions and spatial and temporal scales. Today, data integration processes and methods
are largely ad hoc or manual. A generalized resource infrastructure that integrates knowledge of the data
and the processing tasks being performed by the user in the context of the data and resource lifecycle
is needed. Clouds provide an important infrastructure platform that can be leveraged by including
knowledge for distributed data integration.

4 Application domains

4.1 Main application domains

The Myriads team investigates the design and implementation of system services. Thus its research
activities address a broad range of application domains. We validate our research results with selected
use cases in the following application domains:

• Smart city services,

• Smart grids,

• Energy and sustainable development,

• Home IoT applications,

• Bio-informatics applications,

• Data science applications,

• Computational science applications,

• Numerical simulations.

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5 Social and environmental responsibility

5.1 Footprint of research activities

Anne-Cécile Orgerie is involved in the CNRS GDS EcoInfo that deals with reducing environmental and
societal impacts of Information and Communications Technologies from hardware to software aspects.
This group aims at providing critical studies, lifecycle analyses and best practices in order to reduce the
environmental impact of ICT equipment in use in public research organizations.

5.2 Impact of research results

One of the research axes of the team consists in measuring and decreasing the energy consumption of
Cloud computing infrastructures. The work associated to this axis contributes to increasing the energy
efficiency of distributed infrastructures. This work has been conducted in particular in the CNRS 80Prime
DECORUS project.

In the context of the CNRS RI/RE projects, work is also conducted on the current challenges of the
energy sector and more specifically on the smart digitization of power grid management through the
joint optimization of electricity generation, distribution and consumption. This work aims to optimize
the computing infrastructure in charge of managing the electricity grids: guaranteeing their performance
while minimizing their energy consumption.

In the CNRS FACTO project, the energy aspect of a sustainable smart home is studied by reducing the
oversized number of wireless technologies that is actually connecting all the devices. This work aims to
propose a versatile network based on only one optimized and energy efficient technology (Wi-Fi 7) that
could meet all connected devices requirements.

The Myriads team also engaged in the Inria FrugalCloud challenge, in collaboration with the OVH-
cloud company. The objective is to participate in the end-to-end eco-design of Cloud platforms in order
to reduce their environmental impact.

6 Highlights of the year

• Deborah Agarwal from the Lawrence Berkeley National Laboratory, who held an Inria International
Chair position in the Myriads team from 2014 to 2022, received the Doctorate Honoris Causa from
Université de Rennes on April 4th 2023.

• The Myriads research team reached its end of life on December 31st, 2023. A new team called

Magellan will pursue its research activities starting from January 1st, 2024.

6.1 Awards

• The paper entitled “Latency, Energy and Carbon Aware Collaborative Resource Allocation with
Consolidation and QoS Degradation Strategies in Edge Computing” authored by Emmanuel Gnibga,
Anne Blavette and Anne-Cécile Orgerie received an Outstanding Paper Award at the ICPADS 2023
conference (IEEE International Conference on Parallel and Distributed Systems) [10].

7 New software, platforms, open data

7.1 New software

7.1.1 SimGrid

Keywords: Large-scale Emulators, Grid Computing, Distributed Applications

Scientific Description: SimGrid is a toolkit that provides core functionalities for the simulation of dis-
tributed applications in heterogeneous distributed environments. The simulation engine uses
algorithmic and implementation techniques toward the fast simulation of large systems on a single

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Inria Annual Report 2023

machine. The models are theoretically grounded and experimentally validated. The results are
reproducible, enabling better scientific practices.

Its models of networks, cpus and disks are adapted to (Data)Grids, P2P, Clouds, Clusters and HPC,
allowing multi-domain studies. It can be used either to simulate algorithms and prototypes of
applications, or to emulate real MPI applications through the virtualization of their communication,
or to formally assess algorithms and applications that can run in the framework.

The formal verification module explores all possible message interleavings in the application,
searching for states violating the provided properties. We recently added the ability to assess
liveness properties over arbitrary and legacy codes, thanks to a system-level introspection tool
that provides a finely detailed view of the running application to the model checker. This can for
example be leveraged to verify both safety or liveness properties, on arbitrary MPI code written in
C/C++/Fortran.

Functional Description: SimGrid is a simulation toolkit that provides core functionalities for the simu-

lation of distributed applications in large scale heterogeneous distributed environments.

News of the Year: There were 3 major releases in 2022. The SimDag API for the simulation of the schedul-
ing of Directed Acyclic Graphs has been dropped and replaced by the SimDag++ API which provides
the different features of SimDag directly on top of the S4U API. We also dropped the old and clumsy
Lua bindings to create platforms in a programmatic way. It can be done in C++ in a much cleaner
way now, which motivates this suppression. The C++ platform description has been improved to
reject forbidden topologies, improve exporting for visualization, and allow users to dynamically
change injected costs for MPI_* operations. The Python API to S4U has been extended. A new
solver for parallel task (BMF) has been introduced and provides with more realistic sharing of
heterogeneous resources compared to the fair bottleneck solver used by ptask_L07. Although this
is still ongoing work, this paves the way to efficient macroscopic modeling of streaming activities
and parallel applications. The internals of the Model Checker have been heavily reworked and new
test suites from the MPI Bugs Initiative (MBI) are now used. The documentation was thoroughly
overhauled to ease the use of the framework. We also pursued our efforts to improve the overall
framework, through bug fixes, code refactoring and other software quality improvements.

URL: https://simgrid.org/

Contact: Martin Quinson

Participants: Adrien Lebre, Anne-Cécile Orgerie, Arnaud Legrand, Augustin Degomme, Arnaud Giersch,

Emmanuelle Saillard, Frédéric Suter, Jonathan Pastor, Martin Quinson, Samuel Thibault

Partners: CNRS, ENS Rennes

7.1.2 Tansiv

Name: Time-Accurate Network Simulation Interconnecting Vms

Keywords: Operating system, Virtualization, Cloud, Simulation, Cybersecurity

Functional Description: Tansiv: Time-Accurate Network Simulation Interconnecting Virtual machines
(VMs). Tansiv is a novel way to run an unmodified distributed application on top of a simulated
network in a time accurate and stealth way. To this aim, the VMs execution is coordinated (inter-
rupted and restarted) in order to garantee accurate arrival and transfer of network packets while
ensuring realistic time flow within the VMs. The initial prototype uses Simgrid for simulating
the data transfer and control the execution of the VMs. Also, Qemu is used to encapsulate the
application, intercept the network traffic and enforce the interruption decision. Tansiv can be used
in various situations: malware analysis (e.g. to defeat malware evasion technique based on network
timing measures) or analysis of an application on a geo-distributed context.

Authors: Louis Rilling, Matthieu Simonin, Martin Quinson

Project MYRIADS

Contact: Louis Rilling

Partner: DGA-MI

7.1.3 EnOSlib

13

Name: EnOSlib is a library to help you with your experiments

Keywords: Distributed Applications, Distributed systems, Evaluation, Grid Computing, Cloud comput-

ing, Experimentation, Reproducibility, Linux, Virtualization

Functional Description: EnOSlib is a library to help you with your distributed application experiments.
The main parts of your experiment logic is made reusable by the following EnOSlib building blocks:

- Reusable infrastructure configuration: The provider abstraction allows you to run your experiment
on different environments (locally with Vagrant, Grid’5000, Chameleon and more) - Reusable
software provisioning: In order to configure your nodes, EnOSlib exposes different APIs with
different level of expressivity - Reusable experiment facilities: Tasks help you to organize your
experimentation workflow.

EnOSlib is designed for experimentation purpose: benchmark in a controlled environment, aca-
demic validation . . .

URL: https://discovery.gitlabpages.inria.fr/enoslib/

Publications: hal-01664515, hal-01689726

Contact: Mathieu Simonin

Participants: Mathieu Simonin, Baptiste Jonglez, Marie Delavergne, Alexis Bitaillou

8 New results

8.1 Scaling Clouds

8.1.1 Fog computing platform design

Participants: Chih-Kai Huang, Guillaume Pierre.

There does not yet exist any reference platform for fog computing platforms. We therefore investigated

how Kubernetes could be adapted to support the specific needs of fog computing platforms.

An interesting technology for extending Kubernetes to large-scale geo-distributed scenarios is Kuber-
netes Federations (KubeFed), which allow one to aggregate resources provided by multiple independent
Kubernetes clusters in various locations. We however demonstrated that these federations suffer from
scalability issues due in particular to the amount of monitoring information they need to collect from the
member clusters to make proper scheduling decisions. We therefore proposed a new technique based
on metrics aggregation and deduplication to reduce the volume of monitoring traffic up to 99% without
impacting the quality of scheduling decisions [11]. We also showed how dynamically adapting the metrics
collection frequency allows one to control the tradeoff between communication costs and monitoring
accuracy [12].

We proposed algorithms to allow a fog computing platform based on Kubernetes to dynamically add

and exploit new computing resources in case an overload is detected in some part of the system [7].

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Inria Annual Report 2023

8.1.2 Advanced data management in shared environments

Participants:

Shadi Ibrahim.

Solid State Drives (SSDs) are widely used in data-intensive scenarios due to their high performance
and decreasing cost. However, in shared environments, concurrent workloads can interfere with each
other, leading to a violation of Quality of Service (QoS). While QoS mechanisms like fairness guarantees
and latency constraints have been integrated into SSDs, existing transaction processing frameworks ofer
limited QoS guarantees and can significantly degrade overall performance in a shared environment. The
reason is that the internal components of an SSD, originally designed to exploit parallelism, struggle
to coordinate effectively when QoS mechanisms are applied to them. In this collaborative work, we
propose a novel QoS-enhanced transaction processing framework, called QoS-pro, which enhances QoS
guarantees for concurrent workloads while maintaining high parallelism for SSDs [4]. QoS-pro achieves
this by redesigning transaction processing procedures to fully exploit the parallelism of shared SSDs and
enhancing QoS-oriented transaction translation and scheduling with parallelism features in mind.

The continuous growth in data volume increases the interest in using peer-to-peer (P2P) systems not
only to store static data (i.e., immutable data) but also to store and share mutable data – data that are
updated and modified by multiple users. Unfortunately, current P2P systems are mainly optimized to
manage immutable data. Thus, each modification creates a new copy of the file, which leads to a high
"useless" network usage. Conflict-free Replicated Data Types (CRDTs) are specific data types built in
a way that mutable data can be managed without the need for consensus-based concurrency control.
A few studies have demonstrated the potential benefits of integrating CRDTs in the InterPlanetary File
System (IPFS), an open-source widely used P2P content sharing system. However, they have not been
implemented and evaluated in a real IPFS deployment. This work tries to fill the gap between theory and
practice and provides a quantitative measurement of the performance of CRDTs in IPFS. Accordingly, in
collaboration with the COAST team, we introduce IM-CRDT [8], an implementation of CRDTs in IPFS
that focuses on the simple data type (i.e., Set); and carry out extensive experiments to verify whether
CRDTs can efficiently be utilized in IPFS to handle mutable data. Experiments on Grid’5000 show that
IM-CRDT can sustain low convergence time under concurrent updates.

The high-performance computing I/O stack has been complex due to multiple software layers, the
inter- dependencies among these layers, and the different performance tuning options for each layer. In
this complex stack, the definition of an "I/O access pattern" has been reappropriated to describe what
an application is doing to write or read data from the perspective of different layers of the stack, often
comprising a different set of features. It has become common to have to redefine what is meant when
discussing a pattern in every new study, as no assumption can be made. In the framework of the Hermes
Associate team and the context of the ongoing collaboration between the Myriads project team at Inria
and Lawrence Berkeley National Laboratory, we conduct a comprehensive study and propose a baseline
taxonomy [2], harnessing the I/O community’s knowledge over the past 20 years. This definition can serve
as a common ground for high- performance computing I/O researchers and developers to apply known
I/O tuning strategies and design new strategies for improving I/O performance. We seek to summarize
and bring a consensus to the multiple ways to describe a pattern based on common features already used
by the community over the years.

8.1.3 Geo-distributed graph data processing

Participants:

Shadi Ibrahim.

Graph processing is a popular computing model for big data analytics. Emerging big data applica-
tions are often maintained in multiple geographically distributed (geo-distributed) data centers (DCs)
to provide low-latency services to global users. Graph processing in geo-distributed DCs poses a chal-
lenge when personal data are involved, such as in social network graphs. Indeed, conducting graph
processing algorithms in such a geo-distributed system requires coordination among multiple DCs, while

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simultaneously addressing privacy concerns and complying with different laws and regulations across
countries and regions. With Tristan Allard, Cédric Eichler, Benjamin Nguyen, and Haoying Zhang (a
master intern co-supervised by Myriads, Spicy, and SDS@LIFO), we address these challenges and propose
an innovative framework that enables privacy-preserving geo-distributed graph processing through the
use of synthetic graphs. With this framework, each DC can generate a differentially private view of the
global graph, allowing the application of various graph processing algorithms under the guarantee of
differential privacy.

8.1.4 Resource Allocation for Serverless ML Workflows

Participants:

Shadi Ibrahim.

Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to
benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast
and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model
training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely
on offline prediction, which limit their efficiency. In this collaborative work, we introduce CE-scaling –
a Cost-Efficient autoscaling framework for serverless ML work-flows [20]. During the hyperparameter
tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource
waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during
model training. In this collaborative work, we implement and evaluate CE-scaling on AWS Lambda using
various ML models. Evaluation results show that compared to state-of-the-art static resource allocation
methods, CE-scaling can reduce the job completion time and the monetary cost for hyperparameter
tuning and model training.

8.1.5 Geo-distributed data stream processing

Participants: Khaled Arsalane, Davaadorj Battulga, Alessio Pagliari, Guil-

laume Pierre, Cédric Tedeschi.

Although data stream processing platforms such as Apache Flink are widely recognized as an inter-
esting paradigm to process IoT data in fog computing platforms, the existing performance models that
capture stream processing in geo-distributed environments are theoretical works only, and have not been
validated against empirical measurements. In previous work we developed an auto-scaling mechanism
which can dynamically add or remove resources to adjust the processing capacity to wide variations in
the intensity of the workload. However, in a fog computing environment additional resources may not
always be available. Similarly some streaming applications cannot scale horizontally. In such situations
it becomes useful to exploit transprecision where the precision as well as the compute intensity can
be reduced when necessary. We proposed a dual-controller autoscaler which exploits both horizontal
scalability and transprecision to maintain an application’s processing capacity [15].

In the context of Khaled Arsalane’s PhD thesis, we started exploring the limitations of stream pro-
cessing autoscaling systems when dealing with stateful data processing operators in geo-distributed
environments. A publication on this topic is in preparation.

In the context of Davaadorj Battulga’s PhD thesis, we are exploring mechanisms to bring stream
processing applications in a geo-distributed environment. We base our approach on the collaboration
of multiple, geo-distributed stream processing engines. We worked on the autonomous decentralized
adaptation of such a collaborative platform. In particular, we designed a fully-decentralized adaptation
algorithm for the migration of jobs composing a stream processing pipeline. A software prototype of it
was built and experimented over an emulated geo-distributed platform based on Grid’5000.

8.1.6 Fault tolerance for Function-as-a-Service environments

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Inria Annual Report 2023

Participants:

Yasmina Bouizem, Christine Morin, Nikos Parlavantzas.

Recent years have seen the widespread adoption of serverless computing, and in particular, Function-
as-a-Service (FaaS) systems. One of the main challenges of FaaS systems is providing fault tolerance for
the deployed applications. The basic fault tolerance mechanism in current FaaS platforms is automatically
retrying function invocations. Although the retry mechanism is well-suited for transient faults, it incurs
delays in recovering from other types of faults, such as node crashes. To address this limitation, we
proposed the integration of a Request Replication mechanism in FaaS platforms and described how
this integration was implemented in a well-known, open-source platform. We also provided a detailed
experimental comparison of the proposed mechanism with retry and Active-Standby mechanisms in
terms of performance, availability, and resource consumption under different failure scenarios [3].

8.1.7 Flexible function placement for Function-as-a-Service in the fog

Participants:

Volodia Parol-Guarino, Nikos Parlavantzas.

Function-as-a-Service (FaaS) is a compelling programming model for developing applications that
run on fog infrastructures. FaaS applications are composed of ephemeral, event-triggered functions,
which can be flexibly deployed along the Cloud-to-thing continuum. However, deciding where to place
those functions in the fog poses many challenges, including the heterogeneity and resource constraints
of fog nodes combined with the stringent latency requirements of FaaS applications. Another challenge is
that fog nodes are typically owned by different entities that should be incentivized to share their resources.
To address these challenges, we propose a market-based approach for placing FaaS applications in
the fog. Clients submit function placement requests including latency and resource requirements. The
approach then organizes auctions to determine the nodes that will host the functions and the associated
client payments. We developed an open-source implementation of the framework, called GIRAFF, and
evaluated this on the Grid’5000 testbed. Experiments showed that our framework can reduce client
spending by up to three times while delivering service quality that matches or exceeds that of baseline
methods [16]. This research is part of the thesis project of Volodia Parol-Guarino, started in October 2022.

8.1.8 Balancing performance and sustainability for Function-as-a-Service in the fog

Participants: Mohamed Cherif Zouaoui Latreche, Nikos Parlavantzas.

A main challenge faced by FaaS platforms is effectively balancing application QoS requirements
with the imperative to reduce energy consumption and carbon footprint of fog nodes. This challenge is
exacerbated by the dynamic nature and high deployment density of FaaS workloads, characterized by
short function durations and small function sizes. These factors lead to interferences between workloads,
making it difficult to predict the performance and energy impact of management actions. This research
explores QoS-driven, energy-aware management of FaaS applications in fog environments. To simplify
decision-making for managing FaaS workloads, we are exploring machine learning methods, specifically,
reinforcement learning. This research is part of the thesis project of Mohamed Cherif Zouaoui Latreche,
started in October 2023, in collaboration with Hector Duran-Limon of the University of Guadalajara,
Mexico.

8.1.9 Reliable fog platforms in adverse natural environments

Participants:

Ammar Kazem, Guillaume Pierre.

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17

An interesting use-case for fog computing platforms is environmental monitoring to help Earth Sci-
ences researchers (biologists, hydrologists, etc.) measure and understand specific natural environments.
A fog platform in this context needs to support the needs of a wide range of applications which potentially
compete for limited available computing resources, and exhibit self-management capabilities to remain
operational with minimal human intervention despite potentially adverse execution conditions such as
limited energy resources. In the context of Ammar Kazem’s PhD thesis we started surveying the state of
the art, and organized a survey for better understanding the current and potential future practice of data
management in this domain.

8.1.10 Modeling cloud infrastructures

Participants: Clément Courageux-Sudan, Anne-Cécile Orgerie, Martin Quinson.

Wi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications
at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser.
However, studying their performance at large-scale and in a reproducible manner remains a challenging
task.

This year, we introduced a new Wi-Fi energy model for large-scale simulations. This model, based
on flow-level simulation and integrated in SimGrid, requires fewer computations than state-of-the-art
energy models to estimate bandwidth sharing over a wireless medium, leading to better scalability. The
study shows that our approach yields to performance evaluations that are close to the ones of the classical
ns-3 simulator while improving the runtime of simulations by several orders of magnitude [9].

8.2 Greening Clouds

8.2.1 Energy Models for Cloud infrastructures

Participants:

Anne-Cécile Orgerie, Martin Quinson.

The global energy demand for digital activities is constantly growing. Computing nodes and cloud
services are at the heart of these activities. Understanding their energy consumption is an important step
towards reducing it. On one hand, physical power meters are very accurate in measuring energy but they
are expensive, difficult to deploy on a large scale, and are not able to provide measurements at the service
level. On the other hand, power models and vendor-specific internal interfaces are already available or
can be implemented on existing systems. Plenty of tools, called software-based power meters, have been
developed around the concepts of power models and internal interfaces, in order to report the power
consumption at levels ranging from the whole computing node to applications and services. However, we
have found that it can be difficult to choose the right tool for a specific need. In this work in collaboration
with Avalon and Datamove teams, we qualitatively and experimentally compare several software-based
power meters able to deal with CPU or GPU-based infrastructures [13]. For this purpose, we evaluate
them against high-precision physical power meters while executing various intensive workloads. We
extend this empirical study to highlight the strengths and limitations of each software-based power meter.

8.2.2 End-to-end energy models for the Internet of Things

Participants: Clément Courageux-Sudan, Anne-Cécile Orgerie, Martin Quinson.

The development of IoT (Internet of Things) equipment, the popularization of mobile devices, and
emerging wearable devices bring new opportunities for context-aware applications in cloud computing
environments. The disruptive potential impact of IoT relies on its pervasiveness: it should constitute

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Inria Annual Report 2023

an integrated heterogeneous system connecting an unprecedented number of physical objects to the
Internet. Among the many challenges raised by IoT, one is currently getting particular attention: making
computing resources easily accessible from the connected objects to process the huge amount of data
streaming out of them.

While computation offloading to edge cloud infrastructures can be beneficial from a Quality of Service
(QoS) point of view, from an energy perspective, it is relying on less energy-efficient resources than
centralized Cloud data centers. On the other hand, with the increasing number of applications moving on
to the cloud, it may become untenable to meet the increasing energy demand which is already reaching
worrying levels. Edge nodes could help to alleviate slightly this energy consumption as they could offload
data centers from their overwhelming power load and reduce data movement and network traffic. In
particular, as edge cloud infrastructures are smaller in size than centralized data center, they can make a
better use of renewable energy.

We investigate the end-to-end energy consumption of IoT platforms. Our aim is to evaluate, on con-
crete use-cases, the benefits of edge computing platforms for IoT regarding energy consumption. We aim
at proposing end-to-end energy models for estimating the consumption when offloading computation
from the objects to the Cloud, depending on the number of devices and the desired application QoS.

8.2.3 Exploiting renewable energy in distributed clouds

Participants:

Emmanuel Gnibga, Anne-Cécile Orgerie.

The growing appetite of Internet services for Cloud resources leads to a consequent increase in data
center (DC) facilities worldwide. This increase directly impacts the electricity bill of Cloud providers.
Indeed, electricity is currently the largest part of the operation cost of a DC. Resource over-provisioning,
energy non-proportional behavior of today’s servers, and inefficient cooling systems have been identified
as major contributors to the high energy consumption in DCs.

In a distributed Cloud environment, on-site renewable energy production and geographical energy-
aware load balancing of virtual machines allocation can be associated to lower the brown (i.e. not
renewable) energy consumption of DCs. Yet, combining these two approaches remains challenging in
current distributed Clouds. Indeed, the variable and/or intermittent behavior of most renewable sources
– like solar power for instance – is not correlated with the Cloud energy consumption, that depends on
physical infrastructure characteristics and fluctuating unpredictable workloads [5, 10].

8.2.4 Smart Grids

Participants:

Anne-Cécile Orgerie, Matthieu Silard.

Smart grids allow to efficiently perform demand-side management in electrical grids in order to
increase the integration of fluctuating and/or intermittent renewable energy sources in the energy mix.
In this work, we consider the computing infrastructure that controls the smart grid. In particular, in
the context of the EDEN4SG ANR project, we consider the wide-scale deployment of electrical vehicles.
The energy management of power systems closer-and-closer to real-time will require the intensive use
of pervasive ICT that may suffer from an imperfect quality of service (e.g. delays) which may greatly
decrease the performance of smart grids. They also have an energy and environmental impact. Hence,
the project will consider the “cost of information”.

8.2.5 End-to-end ecodesign of cloud platforms

Participants:

Anne-Cécile Orgerie, Guillaume Pierre, Govind Kovilkkatt Panick-
erveetil.

Project MYRIADS

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In the context of Inria’s challenge on End-to-end ecodesign of cloud platforms, in collaboration with
OVHCloud, we started building an system to automatically deploy data stream processing systems with
their full software ecosystem in a cloud environment. The system uses software energy probes which
can attribute the measured energy consumption to fine-grained elements such as the underlying cloud
infrastructure, the different software elements, etc [14]. We then proceeded to using this tool to study the
main factors which determine a stream processing system’s energy consumption.

Numerous energy, power, and environmental leverages exist and can help cloud providers and data
center managers to reduce some of these impacts. But dealing with such heterogeneous leverages
can be a challenging task that requires some support from a dedicated framework. In collaboration
with OVHCloud, we propose a new approach for modeling, evaluating, and orchestrating a large set of
technological and logistical leverages. Our framework is based on leverages modeling and Gantt chart
leverages mapping. First experimental results based on selected scenarios show the pertinence of the
proposed approach in terms of management facilities and potential impacts reduction [6].

8.3 Experimenting with Clouds

8.3.1 Simulating distributed IT systems

Participants:

Anne-Cécile Orgerie, Martin Quinson.

Our team plays a major role in the advance of the SimGrid simulator of IT systems. This framework
has a major impact on the community. Cited by over 900 papers (60 PhD thesis, 150 journal articles, and
300 conference articles), it was used as a scientific instrument by thousands of publications over the
years.

This year, the work on the framework did not lead to any new publication but instead we pursued
our effort on the technical framework to prepare future scientific endeavors. We worked to ensure that
it correctly captures the concepts needed by the experimenters, in preparation to a post-doctoral work
on the simulation of the computing infrastructure behind the SKA (Square Kilometer Array telescope)
scientific infrastructure starting in January 2023.

Our work on SimGrid is fully integrated into the other research efforts of the Myriads team. This year
our main contribution on this topic was a new energy model of the Wi-Fi networks that is now integrated
to the framework [9]. This enables the study of these networks within the simulator. Along the same line,
the work on the TANSIV project described in the following section also required some adaptation to the
simulator, to further increase the prediction accuracy when exchanging small data packets on local area
networks.

8.3.2 Toward stealth analysis of distributed applications

Participants:

Léo Cosseron, Martin Quinson, Louis Rilling, Matthieu Simonin.

In the TANSIV project we aim at extending the usability of SimGrid to software using arbitrary network
communication APIs and paradigms. For instance this enables SimGrid to run distributed services
implemented in operating systems kernels, such as distributed file systems, and high performance
network applications based on poll-mode network interface card drivers like in the DPDK framework.
To this end we proposed to interconnect SimGrid with Virtual Machine Monitors (VMM) and let all the
network packets output by the virtual machines (VM) flow through SimGrid. This proposal also enhances
SimGrid with applications to security, as the interconnected VMMs can be malware analysis sandboxes.
Thus SimGrid enables malware analysis sandboxes to feature scalable performance-realistic network
environments in order to defeat anti-analysis techniques developed by malware authors.

In 2023 two directions were studied. First, two soundness issues of TANSIV were identified regarding
the simulated network performance. The first issue is a buffer bloat effect caused by the performance
difference between an emulated network interface card and the network link performance simulated by

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Inria Annual Report 2023

the network simulator. The emulated network interface card is indeed only bounded by the CPU speed
and the memory bandwidth, which, without specific precautions, makes the emulated hardware output
packets at much higher speed than the one which should be enforced by the simulation. Thus, network
packets are accumulated between the emulator and the simulator in a large buffer, each packet in the
buffer adding an apparent latency between the source and destination node. Several attempts to solve
this issue while respecting the constraints of not changing the guest software and minimizing changes in
Qemu have been tried during the year and new ones will be considered in 2024.

The second issue is to simulate the effects of network congestion using SimGrid at the packet level
whereas it is a flow-based simulator. In its current models SimGrid considers packets as flows and thus
just slows them down during network congestion. However the real effect of network congestion on
individual packets is to slow them down within the size limits of the queues in the different routers in
the network, and to drop packets once the queues are full. Two approaches are followed to solve this
issue. First TANSIV was adapted to use the ns-3 network simulator instead of SimGrid, which allowed to
do experiments with network congestion. Second a student project was proposed and started to design
SimGrid models which are able to drop packets on congestion.

The second direction studied is using TANSIV with actual malware analysis sandboxes and their tool-
ing. Sandbox tools especially rely on Virtual Machine Introspection (VMI), which consists in intercepting
guest events of interest for the analysis and inspecting the guest state at arbitrary times as well. LibVMI is
a popular library to do VMI and can be used with Qemu-KVM as well as with Xen in the Drakvuf sandbox.
We adapted TANSIV to work with LibVMI on Qemu-KVM and could show that similar mechanisms allow
TANSIV to synchronize VMs with a discrete-event network simulator and hide the guest pauses induced
by VMI activity. This work was submitted for publication (acceptance notification is due in 2024) and
an early version was published as a research report [22]. In parallel we have started porting TANSIV to
the Xen hypervisor, which should allow us to more extensively study and validate the compatibility of
the TANSIV approach and the sandbox tooling that manipulates time in the guest. Porting to the Xen
hypervisor should also allow us to validate the portability of the TANSIV approach between hypervisors.
A paper about the principles introduced for TANSIV was submitted for publication and should benefit
from results of the Xen port.

In addition to the research report published in 2023 [22] a communication of Léo Cosseron was

accepted at the first SOSP Doctoral Workshop of the SOSP conference.

8.3.3 Tools for experimentation

Participants: Matthieu Simonin.

In collaboration with the STACK team and in the context of the Discovery IPL, novel experimenta-
tion tools have been developed. In this context experimenting with large software stacks (OpenStack,
Kubernetes) was required. These stacks are often tedious to handle. However, practitioners need a
right abstraction level to express the moving nature of experimental targets. This includes being able to
easily change the experimental conditions (e.g underlying hardware and network) but also the software
configuration of the targeted system (e.g service placement, fined-grained configuration tuning) and the
scale of the experiment (e.g migrate the experiment from one small testbed to another bigger testbed).

In this spirit we discuss in [23] a possible solution to the above desiderata.
The outcome is a library (EnOSlib) target reusability in experiment driven research in distributed

systems.

The tool is used in several articles (see here). In particular, in [24] the tool is used to build an ad hoc

framework for studying FOG applications.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

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Défi Inria OVHcloud (2021-2025)

Participants:

Anne-Cécile Orgerie, Shadi Ibrahim, Govind Kovilkkatt Panickerveetil,
Guillaume Pierre.

The goal of this collaborative framework between the OVHcloud and Inria is to explore new solutions
for the design of cloud computing services that are more energy-efficient and environment friendly. Five
Inria project-teams are involved in this challenge including Avalon, Inocs, Myriads, Spirals, Stack.

Members of the Myriads team will contribute to four sub-challenges including (1) Software ecodesign
of a data stream processing service; (2) energy-efficient data management; (3) observation of bare metal
co-location platforms and proposal of energy reduction catalogues and models; and (4) modelling and
designing a framework and its environmental Gantt Chart to manage physical and logical levers.

Défi Inria Hive (2022-2026)

Participants:

Shadi Ibrahim, Mohammad Rizk, Guillaume Pierre.

The goal of this collaborative framework between Hive and Inria is to explore new solutions for
the design and realization of large scale secure and reliable Peer-to-Peer Cloud storage. Four Inria
project-teams are involved in this challenge including COAST, Myriads, WIDE, COATI.

Members of the Myriads team will contribute to two axes. Specifically, the Myriads team will coordin-
ate the axis on reliable and cost-efficient data placement and repair in P2P storage over immutable data;
and contribute to the axis on the management of mutable data over P2P storage.

10 Partnerships and cooperations

10.1 National initiatives
ANR FACTO (2021-2024)

Participants:

Anne-Cécile Orgerie, Martin Quinson, François Lemercier.

The number of smart homes is rapidly expanding worldwide with an increasing amount of wireless IT
devices. The diversity of these devices is accompanied by the development of multiple wireless protocols
and technologies that aim to connect them. However, these technologies offer overlapping capabilities.
This overprovisioning is highly suboptimal from an energy point of view and can be viewed as a first
barrier towards sustainable smart homes. Therefore, in the FACTO project, we propose to design a
multi-purpose network based on a single optimized technology (namely Wi-Fi), in order to offer an
energy-efficient, adaptable and integrated connectivity to all smart home’s devices.

ANR EDEN4SG (2023-2027)

Participants:

Anne-Cécile Orgerie.

Climate change as well as geopolitical tensions have led a large number of countries to target a
massive integration of renewables in their energy mix. This will be achieved among others by increasing
the electrification rate of several sectors such as transport. In this context, the wide-scale deployment of
electrical vehicles (EVs) represents a challenge as well as an opportunity to render more efficient and
affordable the transformation of the current power system into a smarter grid. The project targets to
develop methods for the intelligent coordination of large-scale EV fleets and as well to determine the
associated cost of information for piloting the required smart grid.

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Inria Annual Report 2023

ANR Dark-Era (2021-2025)

Participants: Martin Quinson.

The future Square Kilometer Array (SKA) radio telescope poses unprecedented challenges to the
underlying computational system. The instrument is expected to produce a sustained rate of Terabytes of
data per second, mandating on-site pre-processing to reduce the size of data to be transferred. However,
the electromagnetic noise of a traditional computing center would hinder the quality of the measurements
if located near to the instrument. As a result, the Science Data Processor (SDP) pipeline will only have
an energy budget of only 1 MWatt to execute a complex algorithm chain estimated at 250 Petaops/s.
Because of these requirements, the SDP must be an innovative data-oriented infrastructure running on a
disaggregated architecture combining standard HPC systems with dedicated accelerators such as GPU or
FPGA.

The goal of the DarkEra project is to contribute to the performance assessment both in time and energy
of new complex scientific algorithms on not-yet-existing complex computing infrastructures. To that
extend, a prototyping tool will be developed for the prospective profiling of data-oriented applications
during their development.

CARECloud (PEPR Cloud) (2023-2030)

Participants:

Anne-Cécile Orgerie.

Cloud computing and its many variations offer users considerable computing and storage capa-
cities. The maturity of virtualization techniques has enabled the emergence of complex virtualized
infrastructures, capable of rapidly deploying and reconfiguring virtual and elastic resources in increas-
ingly distributed infrastructures. This resource management, transparent to users, gives the illusion
of access to flexible, unlimited and almost immaterial resources. However, the power consumption of
these clouds is very real and worrying, as are their overall greenhouse gas (GHG) emissions and the
consumption of critical raw materials used in their manufacture. In a context where climate change is
becoming more visible and impressive every year, with serious consequences for people and the planet,
all sectors (transport, building, agriculture, industry, etc.) must contribute to the effort to reduce GHG
emissions. Despite their ability to optimize processes in other sectors (transport, energy, agriculture),
clouds are not immune to this observation: the increasing slope of their greenhouse gas emissions must
be reversed, otherwise their potential benefits in other sectors will be erased. This is why the CARECloud
project (understanding, improving, reducing the environmental impacts of Cloud Computing) aims to
drastically reduce the environmental impacts of cloud infrastructures.

NF-JEN (PEPR 5G and future networks) (2023-2030)

Participants:

Anne-Cécile Orgerie.

Communication networks are often presented as a necessary means of reducing the impact on
the environment of various sectors of industry. In practice, the roll-out of new generations of mobile
broadband networks has required increased communication resources for wireless access networks. This
has proved an effective approach in terms of performance but concerns remain about its energy cost
and more generally its environmental impacts. Exposure to electromagnetic fields also remains a cause
of concern despite existing protection limits. In the JEN (Just Enough Networks) project, we propose
to develop just enough networks: network whose dimension, performance, resource usage and energy
consumption are just enough to satisfy users needs. Along with designing energy-efficient and sober
networks, we will provide multi-indicators models that could help policy-makers and inform the public
debate.

Project MYRIADS

23

Taranis (PEPR Cloud) (2023-2030)

Participants:

Shadi Ibrahim, Nikos Parlavantzas, Guillaume Pierre.

New infrastructures, such as Edge Computing or the Cloud-Edge-IoT computing continuum, com-
plicate the cloud landscape as they add new challenges related to resource diversity and heterogeneity
(from small sensors to data centers/HPC, from low power networks to core networks), geographical
distribution, as well as increased dynamicity and security needs, all under energy consumption and
regulatory constraints. In order to efficiently exploit new infrastructures, we propose a strategy based
on a significant abstraction of the application structure description to further automate application and
infrastructure management. Thus, it will be possible to globally optimize used resources with respect
to multi-criteria objectives (price, deadline, performance, energy, etc.) on both the user side (applica-
tions) and the provider side (infrastructures). This abstraction also includes the challenges related to
facilitating application reconfiguration and to automatically adapting the use of resources. The Taranis
project addresses these issues through four scientific work packages, each focusing on a phase of the
application lifecycle: application and infrastructure description models, deployment and reconfiguration,
orchestration, and optimization.

Members of the Myriads team will contribute to four sub-topics including (1) Decentralized, market-
based application orchestration for Fog and IoT environments; (2) Realizing and optimizing Serverless
Computing in the Edge-Cloud continuum; (3) Orchestrating multi-dimensional resources in the Edge-
Cloud continuum; and (4) Resource Provisioning for stream data processing in the Fog.

10.2 Regional initiatives

ARED TDFDE (2022-2025)

Participants: Khaled Arsalane, Guillaume Pierre.

The PhD thesis of Khaled Arsalane is funded at 50% by the TDFDE project under the ARED program
of Région Bretagne. This project investigates performance modeling and optimization of aggregation
operators within geo-distributed data stream processing platforms.

ARED TANSIV (2022-2025)

Participants:

Léo Cosseron, Martin Quinson, Louis Rilling, Matthieu Simonin.

The PhD thesis of Léo Cosseron is funded at 50% by the ARED program "Breizh Cyber Valley" of Région
Bretagne and 50% by the Creach Labs. This project studies how to defeat malware evasion techniques
based on network performance analysis by adding a virtual realistic networks to analysis environments.

11 Dissemination

11.1 Promoting scientific activities

Participants:

Shadi Ibrahim, François Lemercier, Anne-Cécile Orgerie, Nikos Parlav-
antzas, Guillaume Pierre.

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Inria Annual Report 2023

11.1.1 Scientific events: organisation

General chair, scientific chair

• Anne-Cécile Orgerie was conference chair for ICT4S 2023: International Conference on Information

and Communications Technology for Sustainability, Rennes, France.

• Guillaume Pierre is a member of the ACM/IFIP Middleware conference’s Steering Committee.

• Shadi Ibrahim was the Tutorials deputy chair for ISC High Performance 2023, Hamburg, Germany.

• Shadi Ibrahim was a member of the steering committee for the 7th edition of the Workshop

Performance and Scalability of Storage Systems, May 30 2023, Paris.

• Shadi Ibrahim was a member of the steering committee for ISC High Performance 2023, Hamburg,

Germany.

• Shadi Ibrahim is a member of the steering committee of the International Parallel Data Systems

Workshop.

Member of the organizing committees

• Shadi Ibrahim was publicity co-chair for the 53th International Conference on Parallel Processing

(ICPP 2023), 2023, Utah, USA.

11.1.2 Scientific events: selection

Chair of conference program committees

• Guillaume Pierre was PC co-chair of the Future Compute Continuum track of IEEE/ACM CCGrid

2023, and PC co-chair of IEEE IC2E 2023.

• Shadi Ibrahim was program co-chair of the Workshop on Challenges and Opportunities of Efficient

and Performant Storage Systems (CHEOPS@EuroSys2023), Rome, Italy.

Member of the conference program committees

• Anne-Cécile Orgerie was a member of the program committees of IEEE Cluster 2023, IEEE HiPC

2023, and HotCarbon 2023.

• Guillaume Pierre was a member of the program committees of EuroSys 2023, MASCOTS 2023,

IEEE/ACM SEC 2023 and WSCC 2023.

• Shadi Ibrahim was a member of the program committees of SC 2023 (Research papers and research
posters), IEEE IPDPS 2023, IEEE Cluster 2023, IEEE/ACM CCGrid 2023 (SCALE Challenge), Euro-Par
2023, IEEE/ACM UCC 2023, IEEE/ACM PDSW@SC 2023, and QuickPar 2023.

• Nikos Parlavantzas was a member of the program committees of IEEE/ACM UCC 2023, IEEE

CloudCom 2023, IEEE ISPDC 2023, JSSPP’23, and VHPC’23

• François Lemercier was a member of the program committees of Algotel/Cores 2023

Project MYRIADS

25

11.1.3 Journal

Member of the editorial boards

• Anne-Cécile Orgerie is a member of the editorial board of IEEE Transactions on Parallel and

Distributed Systems.

• Shadi Ibrahim is an associate editor of IEEE Internet Computing Magazine.

• Shadi Ibrahim is an associate editor for High Performance Big Data Systems of Frontiers in High

Performance Computing Journal.

• Shadi Ibrahim was a guest editor of IEEE Network – The Magazine of Global Internetworking:

Special Issue on Interplay Between Machine Learning and Networking Systems.

Reviewer - reviewing activities

• Shadi Ibrahim was a reviewer for ACM Transactions on Storage and Springer Future Generation

Computer Systems.

• Nikos Parlavantzas was a reviewer for the Journal of Grid Computing.

11.1.4 Invited talks

• Anne-Cécile Orgerie: “Measuring and modeling the energy consumption of servers”, invited present-
ation at the DIPOpt (Deep learning, image analysis, inverse problems, and optimization) workshop,
Lyon, France, November 30, 2023.

• Anne-Cécile Orgerie: “Network-aware energy-efficient virtual machine management in distributed
Cloud infrastructures with on-site photovoltaic production”, invited presentation at the Workshop
on Scheduling Variable Capacity Resources for Sustainability, Paris, France, March 29, 2023.

• Anne-Cécile Orgerie: “Consommation énergétique et impacts environnementaux des systèmes
distribués”, seminar at IMAG (Institut Montpelliérain Alexander Grothendieck), virtually, December
4, 2023.

• Anne-Cécile Orgerie: “Consommation énergétique et impacts environnementaux des systèmes
distribués”, seminar at ETIS (Equipes Traitement de l’Information et Systèmes), Cergy, March 9,
2023.

• Anne-Cécile Orgerie: “Consommation énergétique et impacts environnementaux des systèmes
distribués”, seminar at Colloquium of LORIA (Laboratoire lorrain de recherche en informatique et
ses applications), Nancy, March 2, 2023.

• Anne-Cécile Orgerie: “Consommation énergétique et impacts environnementaux des systèmes

distribués”, seminar at Federation Normastic day, Caen, February 7, 2023.

• Anne-Cécile Orgerie: “Sciences informatiques écoresponsables”, invited presentation at the INS2I

CNRS conference “Vers une informatique plus durable”, Paris, France, November 27, 2023.

• Anne-Cécile Orgerie: “Impacts environnementaux du numérique”, invited presentation with Olivier

Ridoux at the cati SICPA meeting of INRAE, Saint-Gilles, France, October 4, 2023.

• Anne-Cécile Orgerie: “Sobriété et enjeux technologiques : vers quelle durabilité ?”, workshop chair
at the seminar on planetary boundaries and sustainability stakes of CNRS, Paris, France, July 4,
2023.

• Anne-Cécile Orgerie: “Impacts environnementaux du numérique et structures de recherche asso-
ciées”, invited presentation at the seminar on energy transition and society of CNRS, Paris, France,
April 28, 2023.

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Inria Annual Report 2023

• Anne-Cécile Orgerie: “Sciences informatiques éco-responsables”, invited presentation at the new

recruits day of INS2I, Paris, France, March 20, 2023.

• Shadi Ibrahim: "Scalable and Efficient Big Data Management in Distributed Systems: Addressing
performance variability for Data processing in the Cloud ", invited presentation at the Workshop
on Scheduling Variable Capacity Resources for Sustainability, Paris, France, March 29, 2023.

11.1.5 Leadership within the scientific community

• Anne-Cécile Orgerie is director of the CNRS service group on ICT environmental impact (GDS

EcoInfo).

• Anne-Cécile Orgerie is chief scientist for the Rennes site of Grid’5000.

11.1.6 Scientific expertise

• Anne-Cécile Orgerie was a member of the selection committees for an assistant professor position

at Université Grenoble Alpes and Inria research scientist positions (DR) in 2023.

• Shadi Ibrahim was a member of the ACM Heidelberg Laureate Forum (HLF) 2023 Young Researcher

Selection Committee.

• Shadi Ibrahim was a project reviewer for the French National Research Agency (ANR) AAPG 2023.

11.1.7 Research administration

• Anne-Cécile Orgerie is an officer (chargée de mission) for the IRISA cross-cutting axis on Green IT.

• Anne-Cécile Orgerie was member of the Inria Evaluation Committee.

• Anne-Cécile Orgerie is member of the steering committee of the CNRS GDR RSD.

11.2 Teaching - Supervision - Juries

Participants: Marin Bertier, Shadi Ibrahim, François Lemercier, Anne-Cécile Orgerie,
Nikos Parlavantzas, Jean-Louis Pazat, Guillaume Pierre, Martin Quin-
son, Cédric Tedeschi.

11.2.1 Teaching

• Bachelor: Marin Bertier, Networks, Département Informatique L3, Insa Rennes.

• Bachelor: Marin Bertier, C Language Département Informatique L3, Insa Rennes.

• Bachelor: Marin Bertier, C Language, Département Mathématique L3, Insa Rennes.

• Bachelor: Nikos Parlavantzas, Theoretical and practical study, Département Informatique L3, Insa

Rennes.

• Bachelor: Nikos Parlavantzas, Networks, Département Informatique L3, Insa Rennes.

• Bachelor: Nikos Parlavantzas, Multi-core architectures, Département Informatique L3, Insa Rennes.

• Bachelor: Jean-Louis Pazat, Introduction to programming L1, Département STPI, INSA de Rennes.

• Bachelor: Jean-Louis Pazat, High Performance Computing, Département Informatique L3, Insa

Rennes.

• Bachelor: Jean-Louis Pazat, High Performance Computing, Département Mathematiques L3, Insa

Rennes.

Project MYRIADS

27

• Bachelor: Guillaume Pierre, Systèmes Informatiques, L3 MIAGE, Univ. Rennes 1.

• Bachelor: Guillaume Pierre, Systèmes d’exploitation, L3 Informatique, Univ. Rennes 1.

• Bachelor: Martin Quinson, Architecture et Systèmes, 60 hETD, L3 Informatique, ENS Rennes.

• Bachelor: Martin Quinson, Pedagogy, 15 hETD, L3 Informatique, ENS Rennes.

• Bachelor: Cédric Tedeschi, Cloud and networks, L3, Univ. Rennes 1.

• Bachelor: François Lemercier, Networking, Services and Protocols, L3, Univ. Rennes 1.

• Master: Marin Bertier, Operating Systems, Département Informatique M1, INSA de Rennes

• Master: Marin Bertier, Distributed systems, Département Informatique M2, INSA de Rennes

• Master: Anne-Cécile Orgerie, Green ICT, 4.5 hETD, M2, Telecom SudParis Evry.

• Master: Anne-Cécile Orgerie, Green IT, 6 hETD, M1, INSA Rennes.

• Master: Nikos Parlavantzas, Clouds, M1, INSA Rennes.

• Master: Nikos Parlavantzas, Performance Evaluation, M1, INSA Rennes.

• Master: Nikos Parlavantzas, Operating Systems, M1, INSA Rennes.

• Master: Nikos Parlavantzas, Parallel programming, M1, INSA Rennes.

• Master: Nikos Parlavantzas, Big Data Storage and Processing, M2, INSA Rennes.

• Master: Nikos Parlavantzas, NoSQL, M2, Master for Smart Data Science, ENSAI, Bruz.

• Master: Nikos Parlavantzas, SQL, M2, Master for Smart Data Science, ENSAI, Bruz.

• Master: Nikos Parlavantzas, 4th-year Project, M1, INSA Rennes.

• Master: Jean-Louis Pazat, Parallel Computing, M1 Département Informatique Insa Rennes.

• Master: Jean-Louis Pazat, Internet Of Things, M1 & M2 Département Informatique Insa Rennes.

• Master: Guillaume Pierre, Distributed Systems, M1, Univ. Rennes 1.

• Master: Guillaume Pierre, Service technology, M1, Univ. Rennes 1.

• Master: Guillaume Pierre, Advanced Cloud Infrastructures, M2, Univ. Rennes 1.

• Master: Martin Quinson, C++ system programming (20h ETD), ENS Rennes.

• Master: Martin Quinson, Préparation à l’Agrégation d’Informatique (Networking, 20h ETD), ENS

Rennes.

• Master: Martin Quinson, Scientific Outreach, M2, 30 hEDT, ENS Rennes.

• Master: Cédric Tedeschi, Concurrency in Systems and Networks, M1, Univ. Rennes 1.

• Master: Cédric Tedeschi, Service Technology, M1, Univ. Rennes 1.

• Master: Cédric Tedeschi, Parallel Programming, M1, Univ. Rennes 1.

• Master: Cédric Tedeschi, Advanced Cloud Infrastructures, M2, Univ. Rennes 1.

• Master: Shadi Ibrahim, Cloud Computing and Hadoop Technologies, 36hETD, M2 : Statistics for

Smart Data, ENSAI, Bruz.

• Master: Shadi Ibrahim, Cloud1 (Map-Reduce), 17.5hETD, M2 , IMT-Atlantique, Nantes.

• Master: Shadi Ibrahim, Distributed Big Data, 45hETD, M2, ENSAI, Bruz.

• Master: François Lemercier, Networking, Services and Protocols, M1, Univ. Rennes 1.

• Master: François Lemercier, Software Engineering Project, M1, Univ. Rennes 1.

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Inria Annual Report 2023

11.2.2 Supervision

• PhD defended: Clément Courageux-Sudan, “Reducing the energy consumption of Internet of
Things”, defended in December 2023, supervised by Anne-Cécile Orgerie and Martin Quinson.

• PhD defended: Adrien Gougeon, “Designing an energy-efficient communication network for the
dynamic and distributed control of the electrical grid”, defended in January 2023, supervised by
Anne-Cécile Orgerie and Martin Quinson.

• PhD in progress: Emmanuel Gnibga, “Modeling and optimizing edge computing infrastructures
and their electrical system”, started in November 2021, supervised by Anne-Cécile Orgerie and
Anne Blavette.

• PhD in progress: Vladimir Ostapenco, "Modeling and design of a framework and its Gantt Chart
to manage physical and logical environmental levers", started in December 2021, supervised by
Laurent Lefèvre and Anne-Cécile Orgerie.

• PhD in progress: Maxime Agusti, "Observation of baremetal co-location platforms, models and
catalog proposal to reduce energy consumption", started in December 2021, supervised by Eddy
Caron, Laurent Lefèvre and Anne-Cécile Orgerie.

• PhD in progress: Matthieu Silard, "Co-optimization of electrical and communication networks",
started in February 2023, supervised by Anne-Cécile Orgerie, Nicolas Montavont and Georgios
Papadopoulos.

• PhD in progress: Chih-Kai Huang, "Scalable decentralized fog commputing platforms", started in

2021, supervised by Guillaume Pierre.

• PhD in progress: Khaled Arsalane, "Performance modeling of agregation operators in geo-distributed

data stream processing systems", started in October 2022, supervised by Guillaume Pierre.

• PhD in progress: Govind Kovilkkatt Panickerveetil, "Energy-efficient data stream processing",

started in April 2023, co-supervised by Guillaume Pierre and Romain Rouvoy.

• PhD in progress: Ammar Kazem, "In-natura data processing systems for environmental observation
under energy constraints", started in October 2023, co-supervised by Guillaume Pierre and Laurent
Longuevergne.

• PhD in progress: Davaadorj Battulga, "Stream Processing Pipelines in Fog Environment", started in

September 2018, supervised by Cédric Tedeschi and Daniele Miorandi.

• PhD in progress: Volodia Parol-Guarino, "Flexible resource allocation for FaaS applications in the

fog", started in October 2022, supervised by Nikos Parlavantzas.

• PhD in progress: Mohamed Cherif Zouaoui Latreche, "Balancing Performance and Sustainability
for FaaS in the Fog", started in October 2023, supervised by Nikos Parlavantzas and Hector Duran-
Limon.

• PhD in progress: Mohammad Rizk, “Reliable and cost-efficient data placement and repair in P2P
storage over immutable data”, started in November 2023, supervised by Shadi Ibrahim, Thomas
Lambert, and Guillaume Pierre.

• PhD in progress: Quentin Acher, “Management of mutable data over P2P storage”, started in

September 2023, supervised by Shadi Ibrahim and Claudia-Lavinia Ignat.

• PhD in progress: Mathieu Laurent, “Efficient verification of asynchronous distributed programs“,

started in October 2023, supervised by Martin Quinson and Thierry Jéron.

• PhD in progress: Leo Cosseron, “Time-Accurate Network Simulation Interconnecting VMs with
Hardware Virtualization Towards Stealth Analysis“, started in October 2022, supervised by Martin
Quinson and Louis Rilling.

Project MYRIADS

11.2.3 Juries

29

• Anne-Cécile Orgerie was a reviewer of the PhD manuscript of Edouard Guégain (University of Lille),

September 29, 2023.

• Anne-Cécile Orgerie was a member of the PhD defense of Miguel Felipe Silva Vasconcelos (Univer-

sity Grenoble Alpes), December 20, 2023.

• Guillaume Pierre was a reviewer of the PhD defense of Rafaela Brum (Sorbonne University), Novem-

ber 29th 2023.

• Guillaume Pierre was a member of the PhD defense of Kiranpreet Kaur (Conservatoire National des

Arts et Métiers), September 19th, 2023.

• Martin Quinson was a reviewer of the PhD manuscript of Julien Emmanuel (ENS Lyon), March 8,

2023.

11.3 Popularization

Participants:

Shadi Ibrahim, Anne-Cécile Orgerie, Guillaume Pierre.

11.3.1 Internal or external Inria responsibilities

• Shadi Ibrahim is co-organizing SCI-Rennes seminar: a monthly scientific seminars for all staff at

Inria research centre at Rennes University.

11.3.2 Articles and contents

• Guillaume Pierre participated in an article in "CNRS le journal": Quand le cloud se fait diffus,

January 2023.

11.3.3 Education

• “L codent L créent” is an outreach program to send PhD students to teach Python to middle school
students in 8 sessions of 45 minutes. Tassadit Bouadi (Lacodam), Camille Maumet (Empenn) and
Anne-Cécile Orgerie (Myriads) are coordinating the local version of this program, initiated in Lille.
The first session in Rennes occured in April 2019, and a new session (the 5th) occured in 2023.
The program is currently supported by: Fondation Blaise Pascal, ED MathSTIC, ENS de Rennes,
Université Rennes 1 and Fondation Rennes 1.

12 Scientific production

12.1 Major publications

[1] H. Casanova, A. Legrand, M. Quinson and F. Suter. ‘SMPI Courseware: Teaching Distributed-
Memory Computing with MPI in Simulation’. In: EduHPC-18 - Workshop on Education for High-
Performance Computing. Dallas, United States, Nov. 2018, pp. 1–10. URL: https://hal.inria.fr
/hal-01891513.

12.2 Publications of the year

International journals

[2]

J. L. Bez, S. Byna and S. Ibrahim. ‘I/O Access Patterns in HPC Applications: A 360-Degree Survey’.
In: ACM Computing Surveys (30th July 2023). DOI: 10.1145/3611007. URL: https://hal.scienc
e/hal-04173899.

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Inria Annual Report 2023

[3] Y. Bouizem, D. Dib, N. Parlavantzas and C. Morin. ‘Integrating request replication into FaaS plat-
forms: an experimental evaluation’. In: Journal of Cloud Computing: Advances, Systems and Applic-
ations 12.1 (22nd June 2023), pp. 1–20. DOI: 10.1186/s13677-023-00457-z. URL: https://inr
ia.hal.science/hal-04159510.

[4] H. Fan, Y. Ye, S. Ibrahim, Z. Huang, X. Li, W. Xue, S. Wu, C. Yu, X. Shi and H. Jin. ‘QoS-pro: A
QoS-enhanced Transaction Processing Framework for Shared SSDs’. In: ACM Transactions on
Architecture and Code Optimization (14th Nov. 2023), pp. 1–25. DOI: 10 . 1145 / 3632955. URL:
https://hal.science/hal-04389048.

[5] W. E. Gnibga, A. Blavette and A.-C. Orgerie. ‘Renewable Energy in Data Centers: the Dilemma of
Electrical Grid Dependency and Autonomy Costs’. In: IEEE Transactions on Sustainable Computing
(2023), pp. 1–13. DOI: 10.1109/TSUSC.2023.3307790. URL: https://hal.science/hal-0418
9173.

[6] V. Ostapenco, L. Lefèvre, A.-C. Orgerie and B. Fichel. ‘Modeling, evaluating, and orchestrating
heterogeneous environmental leverages for large-scale data center management’. In: International
Journal of High Performance Computing Applications 37.3-4 (2023). DOI: 10.1177/109434202311
72978. URL: https://hal.science/hal-04047008.

[7] K. Toczé, A. J. Fahs, G. Pierre and S. Nadjm-Tehrani. ‘VioLinn: Proximity-aware Edge Placement
with Dynamic and Elastic Resource Provisioning’. In: ACM Transactions on Internet of Things 4.1
(23rd Feb. 2023), pp. 1–31. DOI: 10.1145/3573125. URL: https://inria.hal.science/hal-03
869221.

International peer-reviewed conferences

[8] Q. Acher, C.-L. Ignat and S. Ibrahim. ‘Quantifying the Performance of Conflict-free Replicated Data
Types in InterPlanetary File System’. In: Middleware 2023 Companion Proceedings. DICG 2023 -
4th International Workshop on Distributed Infrastructure for Common Good. Bologna, Italy, 2023,
pp. 1–6. DOI: 10.1145/3631310.3633488. URL: https://inria.hal.science/hal-04337761.

[9] C. Courageux-Sudan, A.-C. Orgerie and M. Quinson. ‘A Wi-Fi Energy Model for Scalable Simulation’.
In: 24th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks
(WoWMoM). WoWMoM 2023 - 24th IEEE International Symposium on a World of Wireless, Mobile
and Multimedia Networks. Boston (MA), United States, 2023, pp. 1–10. URL: https://hal.scien
ce/hal-04055720.

[10] W. E. Gnibga, A. Blavette and A.-C. Orgerie. ‘Latency, Energy and Carbon Aware Collaborative
Resource Allocation with Consolidation and QoS Degradation Strategies in Edge Computing’. In:
ICPADS 2023 - IEEE International Conference on Parallel and Distributed Systems. ICPADS 2023
- IEEE International Conference on Parallel and Distributed Systems. Hainan, China: IEEE, Dec.
2023, pp. 1–10. URL: https://hal.science/hal-04275783.

[11] C.-K. Huang and G. Pierre. ‘Acala: Aggregate Monitoring for Geo-Distributed Cluster Federations’.
In: SAC 2023 - 38th ACM/SIGAPP Symposium On Applied Computing. Tallinn, Estonia: ACM,
27th Mar. 2023, pp. 1–9. URL: https://inria.hal.science/hal-03899133.

[12] C.-K. Huang and G. Pierre. ‘AdapPF: Self-Adaptive Scrape Interval for Monitoring in Geo-Distributed
Cluster Federations’. In: ISCC 2023 - 28th IEEE Symposium on Computers and Communications.
Tunis, Tunisia: IEEE, 9th July 2023, pp. 1–7. DOI: 10.1145/3555776.3577716. URL: https://inr
ia.hal.science/hal-04103309.

[13] M. Jay, V. Ostapenco, L. Lefèvre, D. Trystram, A.-C. Orgerie and B. Fichel. ‘An experimental compar-
ison of software-based power meters: focus on CPU and GPU’. In: CCGrid 2023 - 23rd IEEE/ACM
international symposium on cluster, cloud and internet computing. Bangalore, India: IEEE, 2023,
pp. 1–13. URL: https://inria.hal.science/hal-04030223.

[14] G. KP, G. Pierre and R. Rouvoy. ‘Studying the Energy Consumption of Stream Processing Engines in
the Cloud’. In: IC2E 2023 - 11th IEEE International Conference on Cloud Engineering. Boston (MA),
United States: IEEE, 25th Sept. 2023, pp. 1–9. URL: https://inria.hal.science/hal-0416407
4.

Project MYRIADS

31

[15] A. Pagliari and G. Pierre. ‘TransScale: Combined-Approach Elasticity for Stream Processing in Fog
Environments’. In: Mobile Cloud 2023 - 11th IEEE International Conference on Mobile Cloud
Computing, Services and Engineering. Athens, Greece: IEEE, 17th July 2023, pp. 1–8. URL: https:
//inria.hal.science/hal-04126361.

[16] V. Parol-Guarino and N. Parlavantzas. ‘GIRAFF: Reverse Auction-based Placement for Fog Func-
tions’. In: WoSC ’23: 9th International Workshop on Serverless Computing. Bologna, Italy: ACM,
11th Dec. 2023, pp. 53–58. DOI: 10.1145/3631295.3631402. URL: https://inria.hal.scienc
e/hal-04384516.

[17]

J. Paturel, C. Quinson, M. Quinson and S. Rokicki. ‘SmolPhone: a smartphone with energy limits’.
In: IGSC 2023 - 14th International Green and Sustainable Computing. Toronto, Canada, 28th Oct.
2023, p. 4. URL: https://inria.hal.science/hal-04156447.

[18] D. Rosendo, K. Keahey, A. Costan, M. Simonin, P. Valduriez and G. Antoniu. ‘KheOps: Cost-effective
Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments’. In: ACM REP ’23:
Proceedings of the 2023 ACM Conference on Reproducibility and Replicability. REP 2023 - ACM
Conference on Reproducibility and Replicability. Santa Cruz, CA, United States: ACM, 28th June
2023, pp. 62–73. DOI: 10.1145/3589806.3600032. URL: https://hal.science/hal-04157720.

[19] H.-Y. Tao, C.-K. Huang and S.-H. Shen. ‘A Low-overhead Network Monitoring for SDN-Based Edge
Computing’. In: ISCC 2023 - 28th IEEE Symposium on Computers and Communications. Tunis,
Tunisia: IEEE, 9th July 2023, pp. 1–6. URL: https://inria.hal.science/hal-04105471.

[20] H. Wu, J. Deng, H. Fan, S. Ibrahim, S. Wu and H. Jin. ‘QoS-Aware and Cost-Efficient Dynamic
Resource Allocation for Serverless ML Workflows’. In: 2023 IEEE International Parallel and Dis-
tributed Processing Symposium (IPDPS). IPDPS - 2023 IEEE International Parallel and Distributed
Processing Symposium. St. Petersburg, United States: IEEE, 18th July 2023, pp. 886–896. DOI:
10.1109/IPDPS54959.2023.00093. URL: https://hal.science/hal-04389016.

Doctoral dissertations and habilitation theses

[21] A. Gougeon. ‘Optimizing a dynamic and energy efficient network piloting the electrical grid’.
Université de Rennes, 11th Jan. 2023. URL: https://theses.hal.science/tel-04086397.

Reports & preprints

[22] L. Cosseron, M. Quinson, L. Rilling and M. Simonin. Hiding Virtual Machine Introspection Pauses
in Networked Sandboxes with Network Simulation. RR-9528. Inria Rennes - Bretagne Atlantique &
IRISA, 13th Nov. 2023, pp. 1–14. URL: https://inria.hal.science/hal-04282253.

12.3 Cited publications

[23] R.-A. Cherrueau, M. Simonin and A. Van Kempen. ‘EnosStack: A LAMP-like stack for the experi-
menter’. In: INFOCOM WKSHPS 2018 - IEEE International Conference on Computer Communica-
tions. Honolulu, United States: IEEE, Apr. 2018, pp. 336–341. DOI: 10.1109/INFCOMW.2018.84070
24. URL: https://hal.inria.fr/hal-01916484.

[24] D. Rosendo, P. Silva, M. Simonin, A. Costan and G. Antoniu. ‘E2Clab: Exploring the Computing
Continuum through Repeatable, Replicable and Reproducible Edge-to-Cloud Experiments’. In:
Cluster 2020 - IEEE International Conference on Cluster Computing. Kobe, Japan, Sept. 2020, pp. 1–
11. DOI: 10.1109/CLUSTER49012.2020.00028. URL: https://hal.archives-ouvertes.fr/h
al-02916032.
<scratchpad>
- Main topic: Research Centre MYRIADS (Inria Centre at Rennes University)
- Key points:
  - Design and implementation of autonomous distributed systems
  - Resource and application management in distributed cloud and fog computing architectures
  - Energy management strategies for greening clouds
  - Security and data protection aspects for securing cloud-based information systems and applications
  - Experimenting with clouds and developing new experimental methodologies
- Structure: The document is organized into sections, including Project-Team, Domain, Theme, Research Program, Application Domains, Social and Environmental Responsibility, Highlights of the Year, New Software, Platforms, Open Data, and Dissemination.
- Potential artifacts to ignore: Web-related elements, such as links, HTML tags, and unnecessary text.
</scratchpad>

<final_summary>
The MYRIADS research centre, located at Rennes University, focuses on designing and implementing autonomous distributed systems. The team tackles the challenges of dependable application execution and efficient resource management in highly distributed clouds. They investigate novel techniques for greening clouds, including the optimization of energy consumption in distributed clouds in the context of smart grids. Additionally, they explore security and privacy issues in cloud-based IoT applications and develop new experimental methodologies for simulating distributed IT systems. The team's research aims to contribute to the design and implementation of simulators well-suited to the study of distributed clouds and large-scale experimentation platforms for distributed systems.
The MYRIADS research centre, located at Rennes University, focuses on designing and implementing autonomous distributed systems. The team tackles the challenges of dependable application execution and efficient resource management in highly distributed clouds. They investigate novel techniques for greening clouds, including the optimization of energy consumption in distributed clouds in the context of smart grids. Additionally, they explore security and privacy issues in cloud-based IoT applications and develop new experimental methodologies for simulating distributed IT systems. The team's research aims to contribute to the design and implementation of simulators well-suited to the study of distributed clouds and large-scale experimentation platforms for distributed systems.