Team PARIS

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Section: Overall Objectives

Middleware systems for computational grids

Computational grids are very powerful machines as they aggregate huge computational resources. A lot of work has been carried out with respect to grid resource management. Existing grid middleware systems mainly focus on resource management like discovery, registration, security, scheduling, etc. However, they provide very little support for grid-oriented programming models.

A suitable grid programming model should be able to take into account the dual nature of a computational grid which is a distributed set of (mainly) parallel resources.

Our general objective is to propose such a programming model and to provide adequate middleware systems. Distributed object or component models seems to be a promising solution. However, they need to be tailored for scientific applications. In particular, the parallel applications have to be encapsulated into objects or components. New paradigms of communication between parallel objects or components have to be designed, together with the required runtime support, deployment facilities, and capacity for dynamic adaptability.

The first issue is the relationship between object or component models, which should handle the distributed nature of grid, and the parallelism of computational codes, which should take into account the parallelism of resources. It is thus required to efficiently integrate both worlds into a coherent, single vision.

The second issue concerns the simplicity and the scalability of communication between parallel codes. As the available bandwidth is larger than what a single resource could consume, parallel communication flows should allow a more efficient utilization of network resources. Advanced flow control should be used to avoid congesting networks. A crucial aspect of this issue is the support for data redistribution involved in the communication between parallel codes.

The third issue refers to the dynamic behavior of applications. While software component models are demonstrating their usefulness in capturing the static architecture of applications, there are still few results on how to deal with the dynamic aspects. The composition operator should be revised so as not to hide such dynamic aspects into the component implementation code.

Promoting a programming model that simultaneously supports distributed as well as parallel middleware systems, independently of the actual resources, raises three new issues. First, middleware systems should be decoupled from the actual networks so as to be deployed on any kind of network. Second, several middleware systems should be able to be simultaneously active within a same process. Third, the solutions to the two previous issues should meet the user requirements for high performance.

The deployment of applications is another issue. Not only is it important to specify the deployment in term of the computational resources (GFlop/s, amount of memory, etc.), but it is also crucial to specify the requirements related to communication resources, such as the amount of bandwidth, or the latency between computational resources. Moreover, we have to deal with applications integrating several distributed middleware systems, like MPI, CORBA, JXTA, etc.

The last issue deals with the dynamic nature of computational grids. As targeted applications may run for very long time, the grid environment is expected to change. Not only middleware systems should support adaptability, but they should also be able to detect variations and to self-adapt. For example, it should be possible to partially redeploy an application on the fly, to benefit from new resources.


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