Current grid-aware applications are implemented on top of low-level libraries by developers who are experts on grid middleware architecture. This approach can hardly support the additional complexity of QoS control in real applications. We discuss a novel approach used in the ASSIST programming environment to implement/guarantee user provided QoS contracts in a transparent and effective way. Our approach is based on the implementation of automatic run-time reconfiguration of ASSIST application executions triggered by mismatch between the user provided QoS contract and the actual performance values achieved.

Schema for Adaptation With the advent of more and more complex and dynamic distributed architectures, such as Computational Grids, growing attention has to be paid to the e#ects of dynamicity on running programs. Even assuming a perfect initial mapping of an application over the computing resources, choices made can be impaired by many factors: load of the used machines and network available bandwidth may vary, nodes can disappear due to network problems, user requirements may change.

Current Grid-aware applications are developed on existing software infrastructures, such as Globus, by developers who are experts on Grid software implementation. Although many useful applications have been produced this way, this approach may hardly support the additional complexity to Quality of Service (QoS) control in real application. We describe the ASSIST programming environment, the prototype of parallel programming environment currently under development at our group, as a suitable basis to capture all the desired features for QoS control for the Grid. Grid applications, built as compositions of ASSIST components, are supported by an innovative Grid Abstract Machine, which includes essential abstractions of standard middleware services and a hierarchical Application Manager, which may be considered as an early prototype of Autonomic Manager.

Previous studies have reported that common dense linear algebra operations do not achieve speed up by using multiple geographical sites of a computational grid. Because such operations are the building blocks of most scientific applications, conventional supercomputers are still strongly predominant in high-performance computing and the use of grids for speeding up large-scale scientific problems is limited to applications exhibiting parallelism at a higher level. We have identified two performance bottlenecks in the distributed memory algorithms implemented in ScaLAPACK, a state-of-the-art dense linear algebra library. First, because ScaLAPACK assumes a homogeneous communication network, the implementations of ScaLAPACK algorithms lack locality in their communication pattern. Second, the number of messages sent in the ScaLAPACK algorithms is significantly greater than other algorithms that trade flops for communication. In this paper, we present a new approach for computing a QR factorization – one of the main dense linear algebra kernels – of tall and skinny matrices in a grid computing environment that overcomes these two bottlenecks. Our contribution is to articulate a recently proposed algorithm (Communication Avoiding QR) with a topology-aware middleware (QCG-OMPI) in order to confine intensive communications (ScaLAPACK calls) within the different geographical sites. An experimental study conducted on the Grid’5000 platform shows that the resulting performance increases linearly with the number of geographical sites on large-scale problems (and is in particular consistently higher than ScaLAPACK’s).

Global computing uses Internet-connected PCs volunteered by their owners. These PCs are diverse, volatile, and error-prone. Sophisticated scheduling methods commonly applied in Grid computing may not be sufficiently scalable and flexible for global computing environments. This paper shows that it is possible to classify global computing hosts based on simple metrics such as availability and reliability, and that it is efficient to assign tasks to such hosts accordingly. The proposed classification of workers is applied to

One of the most promising technical innovations in present-day computing is the invention of grid technologies which harness the computational power of widely distributed collections of computers. However, the programming and optimisation burden of a low level approach to grid computing is clearly unacceptable for large scale, complex applications. The development of grid applications can be simplified by using high-level programming environments. In the present work, we address the problem of the mapping of a high-level grid application onto the computational resources. In order to optimise the mapping of the application, we propose to automatically generate performance models from the application using the process algebra PEPA. We target applications written with the high-level environment ASSIST, since the use of such a structured environment allows us to automate the study of the application more effectively. Our methodology is presented through an example of a classical Divide&Conquer algorithm, together with results which demonstrate the efficiency of this approach.

Abstract. In this paper a model of grid computation that supports both heterogeneity and dynamicity is presented. The model presupposes that user sites contain software components awaiting execution on the grid. User sites and grid sites interact by means of managers which control dynamic behaviour. The orchestration language ORC [9,10] offers an abstract means of specifying operations for resource acquisition and execution monitoring while allowing for the possibility of non-responsive hardware. It is demonstrated that ORC is sufficiently expressive to model typical kinds of grid interactions.

Keywords: Nowadays, component application adaptivity in Grid environments has been afforded in different ways, such those provided by the Dynaco/AFPAC framework and by the ASSIST environment. We propose an abstract schema that catches all the designing aspects a model for parallel component applications on Grid should define in order to uniformly handle the dynamic behavior of computing resources within complex parallel applications. The abstraction is validated by demonstrating how two different approaches to adaptivity, ASSIST and Dynaco/AFPAC, easily map to such schema. Abstract schema, component adaptivity, Grid parallel component application. 90 INTEGRATED RESEARCH IN GRID COMPUTING 1. An Abstract Schema for Adaptation Adaptivity is a concept that recent framework proposals for Computational Grid take into great account. In fact, due to the unstable nature of the Grid (nodes that disappear because of network problems, changes in user requirements/computing

The development of efficient grid applications usually requires writing huge portions of code directly at the level of abstraction provided by the underlying grid middleware. In this work we discuss an alternative approach, raising the level of abstraction used when programming grid applications. Our approach requires programmers just to describe in a qualitative way the kind of parallelism they want to express. Then, compiler tools, loader tools and run time system take complete care of running the application on a grid target architecture. This allows to move most of the cumbersome tasks related to grid targeting and management from programmer responsibility to tools. This paper introduces the structured parallel programming environment ASSIST, whose design is aimed at raising the level of abstraction in grid programming and discusses how it can support transparent grid programming while implementing grid adaptivity.

Grid is a non-dedicated and dynamic computing environment. Consequently, different programs have to compete with each other for the same resources, and resource availability varies over time. That causes the performance of user programs are degraded and unpredictable. For resolving this problem, we propose a multilayer resource reconfiguration framework for grid computing. As the named, this framework adopts different resource reconfiguration mechanisms for different workloads of resources. We have implemented this framework on a grid-enabled DSM system called Teamster-G. Our experimental result shows that our proposed framework allows Teamster-G not only to fully utilize abundant CPU cycles but also to minimize resource contention between the jobs of resource consumers and those of resource providers. As a result, the job throughput of Teamster-G is effectively increased.