MESCAL
Current National Academic Projects
ANR MARMOTE (2013-2016) (ANR MN)
The project aims at realizing the prototype of a software environment dedicated to modeling with Markov chains. It brings together seven partner teams, expert in markovian analysis, who will develop advanced solution algorithms and applications in different scientific domains: reliability, distributed systems, biology, physics and economics. The MARMOTE project involves researchers from Laboratories and Universities of Montpellier, Grenoble, Versailles, Paris. The permanent researchers from MESCAL involved in this project are Bruno Gaujal, Jean-Marc Vincent and Florence Perronnin.ANR SONGS (2012-2015)
The last decade has brought tremendous changes to the characteristics of large scale distributed computing platforms. Large grids processing terabytes of information a day and the peer-to-peer technology have become common even though understanding how to efficiently such platforms still raises many challenges. As demonstrated by the USS SimGrid project funded by the ANR in 2008, simulation has proved to be a very effective approach for studying such platforms. Although even more challenging, we think the issues raised by petaflop/exaflop computers and emerging cloud infrastructures can be addressed using similar simulation methodology. The goal of the SONGS project is to extend the applicability of the SimGrid simulation framework from Grids and Peer-to-Peer systems to Clouds and High Performance Computation systems. Each type of large-scale computing system will be addressed through a set of use cases and lead by researchers recognized as experts in this area. The SONGS project involves researchers from Laboratories and Universities of Nancy, Grenoble, Villeurbanne, Bordeaux, Strasbourg, Nantes, and Nice. The researchers from MESCAL involved in this project are Arnaud Legrand, Derrick Kondo, Jean-Marc Vincent, and Jean-François Méhaut.SPADES, 2009-2012, ANR SEGI
Partners: INRIA GRAAL, INRIA GRAND-LARGE, CERFACS, CNRS, INRIA PARIS, LORIA
Petascale systems consisting of thousands to millions of resources have emerged. At the same, existing infrastructure are not capable of fully harnessing the computational power of such systems. The SPADES project will address several challenges in such large systems. First, the members are investigating methods for service discovery in volatile and dynamic platforms. Second, the members creating novel models of reliability in PetaScale systems. Third, the members will develop stochastic scheduling methods that leverage these models. This will be done with emphasis on applications with task dependencies structured as graph.
Clouds@home, 2009-2013 ANR Jeunes Chercheurs
The overall objective of this project is to design and develop a cloud computing platform that enables the execution of complex services and applications over unreliable volunteered resources over the Internet. In terms of reliability, these resources are often unavailable 40\% of the time, and exhibit frequent churn (several times a day). In terms of "real, complex services and applications", we refer to large-scale service deployments, such as Amazon's EC2, the TeraGrid, and the EGEE, and also applications with complex dependencies among tasks. These commercial and scientific services and applications need guaranteed availability levels of 99.999\% for computational, network, and storage resources in order to have efficient and timely execution.
USS Simgrid, 2009-2012, ANR SEGI
Partners:INRIA Nancy, INRIA Saclay, INRIA Bordeaux, University of Reims, IN2P3, University of Hawaii at Manoa
The goal of the USS-SimGrid project is to allow scalable and accurate simulations by means of the SimGrid simulation toolkit. This toolkit is widely used for simulation of HPC systems. We aim to extend the functionality of the toolkit to enable the simulation of heterogeneous systems with more than tens of thousands of nodes. There three main thrusts in this project. First, we will improve the models used in SimGrid, increasing their scalability and easing their instanciation. Second, we will develop tools that ease the analysis of detailed and large simulation results, and aid the management of simulation deployments. Third, we will improve the scalability of simulations using parallelization and optimization methods.
Past National Academic Projects
PROHMPT, 2009-2011, ANR COSI
Partners: BULL SAS, CAPS entreprise, CEA CESTA, CEA INAC, INRIA RUNTIME, UVSQ PriSM
Processor architectures with many-core processors and special-purpose processors such as GPUS and the CELL processor have recenty emerged. These new and heterogeneous architectures require new applicaton programming methods and new programming models. The goal of the ProHMPT project is to address this challenge by focusing on the immense computing needs and requirements of real simulations for nanotechnologies. In order for nanosimulations to fully leverage heterogeneous computing architectures, project members will novel technologies at the compiler, runtime, and scientific kernely levels with proper abstractions and wide portability. This project brings experts from industry, in particular HPC hardware expertise from BULL and nanosimulation expertise from CEA.
PEGASE, 2009-2011, ANR ARPEGE
Partners: RealTimeAtWork, Thales, ONERA, ENS Cachan
The goal of this project to achieve performance guarantees for communicating embedded systems. Members will develop mathematical methods that give accurate bounds on maximum network delays in both space and aviation systems. The mathematical methods will be based on Network Calculus theory, which is type of queuing theory that deals with worst-case performance evaluation. The expected results will be novel models and software tools validated in mission-critical real-time embedded networks of the aerospace industry.ANR DOCCA (2007-2011)
The recent evolutions in computer networks technology, as well as their diversification, yield a tremendous change in the use of these networks: applications and systems can now be designed at a much larger scale than before. This scaling evolution is dealing with the amount of data, the number of computers, the number of users, and the geographical diversity of theses users. Up until now, main achievements based on the peer-to-peer paradigm mainly concern file-sharing issues. We believe that a large class of scientific computations could also take advantage of this kind of organization. This project is thus about the design of a peer-to-peer computing infrastructure with a particular emphasis on the fairness issues. The researchers from MESCAL involved in this project are: Florence Perronnin, Arnaud Legrand, and Corinne Touati.Check-bound (2007-2010), ANR SETIN
Partners: University of Paris I.The increasing use of computerized systems in all aspects of our lives gives an increasing importance on the need for them to function correctly. The presence of such systems in safety-critical applications, coupled with their increasing complexity, makes indispensable their verification to see if they behaves as required . Thus the model checking which is the automated manner of formal verification techniques is of particular interest. Since verification techniques have become more efficient and more prevalent, it is natural to extend the range of models and specification formalisms to which model checking can be applied. Indeed the behavior of many real-life processes is inherently stochastic, thus the formalism has been extended to probabilistic model checking. Therefore, different formalisms in which the underlying system has been modeled by Markovian models have been proposed.
Stochastic model checkng can be performed by numerical or statistical methods. In model checking formalism, models are checked to see if the considered measures are guaranteed or not. We apply Stochastic Comparison technique for numerical stochastic model checking. The main advantage of this approach is the possibility to derive transient and steady-state bounding distributions as well as the possibility to avoid the state-space explosion problem. For the statistical model checking we study the application of perfect simulation by coupling in the past. This method has been shown to be efficient when the underlying system is monotonous for the exact steady-state distribution sampling. We consider to extend this approach for transient analysis and to model checking by means of bounding models and the stochastic monotonicity. As one of the most difficult problems for the model checking formalism, we also study the case when the state space is infinite. In some cases, it would be possible to consider bounding models defined in finite state space.
Members of MESCAL involved in this project are Jean-Marc Vincent and Bruno Gaujal.
ANR blanche MEG (2007-2010)
The "ANR blanche" MEG, is composed of two teams: physicists working on electromagnetism from the LAAS (Toulouse) and the MESCAL project-team. The main objective is to study scaling properties in electromagnetism simulation applications and grids. The first results are promising. They demonstrate that the tools developed by Mescal on large data storage and middleware for deployment on clusters and grids are appropriate for that kind of application.
Yves Denneulin is the main members of MESCAL involved in this project.
ARC POPEYE (2008-2009)
The ARC Popeye focuses on the behavior of large complex systems that involve interactions among one or more populations. By population we mean a large set of individuals, that may be modeled as individual agents, but that we will often model as consisting of a continuum of non-atomic agents. The project brings together researchers from different disciplines: computer science and network engineering, applied mathematics, economics and biology. This interdisciplinary collaborative research aims at developing new theoretical tools as well as at their applications to dynamic and spatial aspects of populations that arise in various disciplines, with a particular focus on biology and networking.The researchers from MESCAL involved in this project are: Corinne Touati, Arnaud Legrand, and Bruno Gaujal
Grappe200 project
MENRT-UJF-INPG, Rhône-Alpes Region, INRIA , ENS-Lyon have funded a cluster composed of 110 bi-processors Itanium2 connected with a Myrinet (donation of MyriCom) high performance network. This project is lead by MESCAL, MOAIS, ReMaP and SARDES. It is part of the CIMENT project which aims at building high performance distributed grids between several research labs.
DSLLab (2005-2009), ANR Jeunes Chercheurs
Partners: INRIA-FUTURS.DSLlab is a research project aiming at building and using an experimental platform about distributed systems running on DSL Internet. The objective is twofold:
- provide accurate and customized measures of availability, activity and performances in order to characterize and tune the models of the ASDL resources;
- provide a validation and experimental tool for new protocols, services and simulators and emulators for these systems.
NUMASIS (2005-2009), ANR Calcul Intensif et Grilles de Calcul
Future generations of multiprocessors machines will rely on a NUMA architecture featuring multiple memory levels as well as nested computing units (multi-core chips, multi-threaded processors, multi-modules NUMA, etc.). To achieve most of the hardware's performance, parallel applications need powerful software to carefully distribute processes and data so as to limit non-local memory accesses. The ANR NUMASIS(NUMASIS: Adapting and Optimizing Applicative Performance on NUMA Architectures: Design and Implementation with Applications in Seismology) project aims at evaluating the functionalities provided by current operating systems and middleware in order to point out their limitations. It also aims at designing new methods and mechanisms for an efficient scheduling of processes and a clever data distribution on such platforms. These mechanisms will be implemented within operating systems and middleware. The target application domain is seismology, which is very representative of the needs of computer-intensive scientific applications.
Jean-François Méhaut, from MESCAL, is involved in this project.
Aladdin-G5K, 2008-2011, ADT INRIA
After the success of the Grid'5000 project of the ACI Grid initiative led by the French ministry of research, INRIA is launching the ALADDIN project to further develop the Grid'5000 infrastructure and foster scientific research using the infrastructure. ALADDIN will build on Grid'5000's experience to provide an infrastructure enabling computer scientists to conduct experiments on large scale computing and produce scientific results that can be reproduced by others. ALADDIN focus on the following challenges :
- Transparent, safe and efficient large scale system utilization and programming
- Providing service agreement to users in large scale parallel and distributed systems
- Providing confidence to the user about the infrastructure
- Efficient exploitation of highly heterogeneous and hierarchical large-scale systems
- Efficient and scalable composition and orchestration of services
- Modeling of large scale systems and validation of their simulators
- Scalable applications for large scale systems
- Dynamic interconnection of autonomous and heterogeneous resources
- Efficiently manage very large volumes of information (search, mining, classification, secure storage and access, etc) for a wide spectrum of applications areas (web applications, image processing, health, environment, etc).
Aleae, 2009-2010, ARC INRIA
Partners: INRIA ALGORILLE, INRIA GRAAL, INRIA MESCAL, TU Delft.
The MESCAL project-team participates in the ALEAE project of the INRIA ARC program. This project is led by Emmanuel Jeannot of the INRIA ALGORILLE project-team, who recently moved to the RUNTIME project-team. The project's goal is to provide models and algorithmic solutions in the field of resource management that cope with uncertainties in large-scale distributed systems. This work is based on the Grid Workloads Archive designed at TU Delft, Netherlands. Resulting from this collaboration, we have created the Failure Trace Archive, which is a repository of availabilty traces of distributed systems, and analytical tools. Moreover, we are conducting trace-driven experiments to test our solutions, to validate the proposed models, and to evaluate the algorithms. These experiments are being conducted using simulators and large-scale environments such as Grid'5000 in order to improve both models and algorithms.
