Team grand-large

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Overall Objectives
Scientific Foundations
Application Domains
Software
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Other Grants and Activities
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Section: Scientific Foundations

Methodology and Technologies for Large Scale Distributed Systems

Research in the context of LSDS involves understanding large scale phenomena from the theoretical point of view up to the experimental one under real life conditions. The general research context should also considers the fundamental technological trend toward a convergence between Grid and P2P systems.

Metodology

One key aspects of the impact of large scale on LSDS is the emergence of phenomena which are not coordinated, intended or expected. These phenomena are the results of the combination of static and dynamic features of each component of LSDS: nodes (hardware, OS, workload, volatility), network (topology, congestion, fault), applications (algorithm, parameters, errors), users (behavior, number, friendly/aggressive).

Grand-Large aims at gathering several complementary techniques to study the impact of large scale in LSDS: theoretical models, simulation, emulation and experimentation on real platforms. Fundamental aspects of LSDS as well as the development of middleware platforms are already existing in Grand-Large. We are also involved in the development and deployment of simulators and emulators and real platforms (testbed).

We are currently developing a simulator of LSDS called V-Grid aiming at discovering, understanding and managing implicit uncoordinated large scale phenomena. Several Grid simulators have been developed by other teams: SimGrid [68] GridSim [65] , Briks [53] . All these simulators considers relatively small scale Grids. They have not been designed to scale and simulate 10 K to 100 K nodes. Other simulators have been designed for large multi-agents systems such as Swarm [96] but many of them considers synchronous systems where the system evolution is guided by phases. V-Grid is built from Swarm and adds asynchrony in the simulator, node volatility and a set of specialized features for controlling and measuring the simulation of LSDS. To exemplify the need of such simulator, we are first considering the fully distributed scheduling problem. Using V-Grid for comparing several algorithms, we have already demonstrate the need for complementary visualization tools, showing the evolution of key system parameters, presenting the distributed system topology, nodes and network global trends in a 2 dimensional shape and presenting the dynamics of the system component activity in a 3 dimensional shape. Using this last representation, we have discover unexpected large scale phenomena which would be very difficult to predict by a theoretical analysis of the simulated platform features and the scheduling algorithms.

Emulation is another tool for experimenting systems and networks with a higher degree of realism. Compared to simulation, emulation can be used to study systems or networks 1 or 2 orders of magnitude smaller in terms of number of components. However, emulation runs the actual OS/middleware/applications on actual platform. Compared to real testbed, emulation considers conducting the experiments on a fully controlled platform where all static and dynamic parameters can be controlled and managed precisely. Another advantage of emulation over real testbed is the capacity to reproduce experimental conditions. Several implementations/configurations of the system components can be compared fairly by evaluating them under the similar static and dynamic conditions. Grand-Large is leading one of the largest Emulator project in Europe called Grid explorer. This project uses a 1K CPUs cluster as hardware platform and gathers 24 experiments of 80 researchers belonging to 13 different laboratories. Experiments concern developing the emulator itself and use of the emulator to explore LSDS issues. ( http://www.lri.fr/~fci/GdX/ ).

Grand-Large members are also involved in the French Grid 5000 project which intents to deploy an experimental Grid testbed for computer scientists. This testbed may feature up to 5000 K CPUs gathering the resources of about 10 clusters geographically distributed over France. The clusters will be connected by a high speed network (Renater or/and other). Grand-Large is a leading team in Grid 5000, chairing the eGrid 5000 Specific Action of the CNRS which is intended to prepare the deployment and installation of Grid 5000. eGrid 5000 gathers about 30 engineers, researchers and team directors who have frequent meetings, discussing about the testbed security infrastructure, experiment setup, cluster coordination, experimental result storage, etc. ( http://www.lri.fr/~fci/AS1/ ).

Technological Trends

The development of LSDS has followed a trajectory parallel to the one of Grid systems such as Globus [81] and Unicore [77] . Nevertheless we can observe some convergence elements between LSDS and Grid. The paper [80] gives many details about the similarities and differences between P2P and Grid systems. From the technological perspective, the evolution of Globus to GT3 [82] with the notion of Grid services is one reason of this convergence. The evolution of LSDS toward more generic and secure systems being able to provide CPU, storage and communication sharing among participants is another element of this convergence, since the notion of controllable services is likely to emerge from this perspective of more generality and flexibility.

Nowadays, Grid Computing is considering the notion of services through OGCSA [82] and OGSI [110] . A Grid service is an entity that must be auto-descriptive, dynamically published, creatable and destructible, remotely invoked and manageable (including life time cycle). The standardization effort also includes the use of well defined standards (WSDL, SOAP, UDDI...) of Web Services [113] . A typical LSDS platform gathering client nodes submitting requests to a coordination service which schedules them on a set of participating nodes can be implemented in term of services: the coordination service publishes application services and schedules their instantiations on workers; the client service requests task (association of application and parameters) executions corresponding to published application services and collects results from the coordination service; the worker service computes tasks and sends their results back to the coordination service. Note that the implementation of the coordination service can rely on sub-services such as a scheduler, a data server for parameters and results, a service repository/factory which themselves may be implemented in centralized or distributed way.

Thus we believe that LSDS could benefit from the standardization effort conducted in the Grid context by reusing the same concepts of services and by adopting the same standards (OGSA and OGSI). For example, the next version of XtremWeb will be implemented by a set of Grid services.


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