Overview of grid / cloud research in France

Michel DAYDÉ

Scientific Delegate at INS2/CNRS in charge of HPC / Grid / cloud

Université de Toulouse - IRIT

Overview of grid / cloud research in France

Distributed infrastructures attract considerable attention since 20 years

Most of the grid / cloud research is developed by computer science teams and mainly structured around Grid'5000

Links with traditional production grids, high performance computing, large scale data processing data, … exist but should be reinforced on some aspects … and France Grilles can play a role

Most of the data presented here come from a report issued in summer 2010

Large scale distributed infrastructures : main research topics

NB: Grid / cloud are a subset of that area Software environment: middleware, efficient

exploitation, distributed systems and objects, management of large amount of data, security issues, virtualization, reconfiguration, autonomic administration

Services : computing oriented service, management, dynamic adaptation, workflows

Distributed applications: new concepts, languages and tools, contexte-sensitive applications, abstraction, models, ...

Main research topics (con't)

Object-oriented software architectures / software components for systems with distributed intelligence

Algorithms and scheduling: distributed algorithms, P2P, large scale computing, combinatorial and NP-hard problems,

Conception of distributed applications, concurrent programming, proof, refinement and development of parallel programs, use of formal languages, models, ...

People involved

Approximatively 530 researchers with:

450 university lecturers / professors Around 50 INRIA researchers Around 30 CNRS researchers

Grid / cloud research topics

Software environment for grids: middleware, languages, libraries, high performance execution layers, networks, communications, energy, heterogeneity, …

Management of resources: virtualization, autonomic administration, workflows...,

Management of data in large scale distributed systems (clusters, grids, P2P, cloud), distributed storage

Scalability

Grid / cloud research topics (con't)

Algorithms and scheduling High performance networks Modelling and use of large scale distributed

data and computing infrastructures Gridification / cloudification of applications:

simulations and numerical algorithms on grids in various areas (environmental issues, electromagnetism, chemical engineering, biology and health, security, linear algebra, …)

Pervasive grids

People involved Around 310 researchers:

238 university lecturers / professors 39 INRIA researchers 21 CNRS researchers

12% of INRIA, 3% of CNRS and 7% of university members in Section 7 (Science and Technology of Information i.e. computer science, automatic, signal and communication) of CNRS work on Grid / Cloud

More generally 12% of researchers are working on large scale distributed infrastructures

Section 7 in CNRS has 605 CNRS, 316 INRIA and 3358 lecturer / professors.

Structuration of grid / cloud research in France

In the HEMERA initiative: 21 research / project teams all located in

CNRS labs including 15 INRIA projects (the six non INRIA project teams are located in Strasbourg and Toulouse).

Around 174 researchers with 119 lecturers/professors, 38 INRIA, 17 CNRS

Approximatively 3/5 of grid / cloud research is structured around GRID'5000.

HEMERA research teams Bordeaux : CEPAGE and RUNTIME (LaBRI). Grenoble :MESCAL (LIG). Lille : DOLPHIN (LIFL). Lyon : GRAAL and RESO (LIP). Nantes : ASCOLA (LINA) Paris : GRAND-LARGE (LRI) and REGAL (LIP6). Rennes : ASAP, KERDATA, MYRIADS, SAGE (IRISA). Nice-Sophia : OASIS (I3S). Strasbourg : ALGORILLE (LORIA), ICPS (LSIIT). Toulouse : ACADIE , APO , ASTRE (IRIT), MINC and MRS

(LAAS)

Other teams not involved in HEMERA

Team of David Hill (LIMOS, Univ. Clermont). AND and CARTOON (LIFC, Univ. Franche-Comté) MAP (LIFL, Lille). ATLAS-GDD (LINA, Nantes). PRV (LIFO, Orléans). Groupe Grilles (LAL, Orsay). PEQUAN (LIP6, Paris). T2I (LMA, Univ. Pau), Groupe SysCom (CRESTIC, Univ. Reims). MODALIS (I3S, Sophia).

Emerging topics in Grid / Cloud

- Modelling large scale distributed architectures, experimental methodology (reproducibility, …)

- Security on large scale platforms (clusters, grids, clouds, networks of sensors, …)

- Taking dynamicity into account at all levels: virtualization, autonomic administration, monitoring, automation of processus, …

- Mastering scalability

- Power-aware management of resources: algorithms, scheduling, monitoring, …. Green-IT

More specifically for the Cloud

- Cloud architecture and provisiong

- Grid / Cloud and private / public cloud interoperability

- Cloud and HPC

- Programming models, applications and middleware for dynamic environments

- New interfaces and browsers for accessing the Cloud: interaction with mobiles, Ecommerce, ...

Links with large data processing community

- Interactions with research community working on large scale amount of data should be developped. Some of the key topics are:

– data management (centralized / distributed DB, datawarehouse, storage virtualization, ...), indexation and information retrieval

– data processing and knowlede discovery on large scale distributed platforms (e.g.LHC), data mining, classification, data assimilation, ….

– post-processing: 3D vizualisation, virtual reality, use of surrogate models, …....

- Around 470 researchers

Links with HPC

- Grids are also a tool for HPC and both communities should have more interaction (Comité Stratégique du Calcul intensif, 2008)

- Example: grids are very useful for specific simulations or processing large amounts of data generated by simulations

- HPC research in France is around 370 researchers (2/3 are in mathematics, 1/3 in computer science, a few teams in engineering)

Conclusion

- More than 10% of computer science researchers (>500) are involved in topics related to large scale distributed infrastructures both at theoretical and practical levels

- 3/5 of these researchers (>300) are working on grid /cloud

- Connexions between research grid and production grid and with HPC / large scale data processing should be enhanced