While distributed, heterogeneous collections of computers ("Grids") can in principle be used as a computing platform, in practice the problems of first discovering and then configuring resources to meet application requirements are difficult problems. We present a general-purpose resource selection framework that addresses these problems by defining a resource selection service for locating Grid resources that match application requirements. At the heart of this framework is a simple but powerful declarative language based on a technique called set matching, which extends the Condor matchmaking framework to support both single resource and multiple resource selection. This framework also provides an open interface for loading application-specific mapping modules to personalize the resource selector. We present results obtained when this framework is applied in the context of a computational astrophysics application, Cactus. These results demonstrate the effectiveness of our technique.

In this paper, we investigate G-commerce — computational economies for controlling resource allocation in Computational Grid settings. We define hypothetical resource consumers (representing users and Grid-aware applications) and resource producers (representing resource owners who “sell ” their resources to the Grid). We then measure the efficiency of resource allocation under two different market conditions: commodities markets and auctions. We compare both market strategies in terms of price stability, market equilibrium, consumer efficiency, and producer efficiency. Our results indicate that commodities markets are a better choice for controlling Grid resources than previously defined auction strategies. 1

In this paper, we consider the problem of modeling machine availability in enterprise-area and wide-area distributed computing settings. Using availability data gathered from three different environments, we detail the suitability of four potential statistical distributions for each data set: exponential, Pareto, Weibull, and hyperexponential. In each case, we use software we have developed to determine the necessary parameters automatically from each data collection.

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The ability to harness heterogeneous, dynamically available Grid resources is attractive to typically resource-starved computational scientists and engineers, as in principle it can increase, by significant factors, the number of cycles that can be delivered to applications. However, new adaptive application structures and dynamic runtime system mechanisms are required if we are to operate effectively in Grid environments. In order to explore some of these issues in a practical setting, we are developing an experimental framework, called Cactus, that incorporates both adaptive application structures for dealing with changing resource characteristics and adaptive resource selection mechanisms that allow applications to change their resource allocations (e.g., via migration) when performance falls outside specified limits. We describe here the adaptive resource selection mechanisms and describe how they are used to achieve automatic application migration to better resources following performance degradation. Our results provide insights into the architectural structures required to support adaptive resource selection. In addition, we suggest that this Cactus Worm is an interesting challenge problem for Grid computing.

In this chapter, we describe the use of economic principles as the basis for Grid resource allocation policies and mechanisms. A computational economy in which users “buy ” resources from their owners is an attractive method of controlling Grid resource allocation for several reasons. Economies are intuitively easy to understand, they fit the model of flexible resource usage under local control (which is fundamental to Grid computing), and they can be analyzed through a considerable body of extant theory. We discuss many of the fundamental characteristics of computational economies, particularly as they pertain to Grid computing. We also present G-commerce — a framework that we have used to investigate Grid resource economies — as an example of the type of results that are possible. Finally, we discuss several of the issues associated with empirical investigation of Grid economies as a motivation for future work. 1

As machines and programs have become more complex, the process of programming applications that can exploit the power of high-performance systems has become more difficult and correspondingly more labor-intensive. This has substantially widened the software gap the discrepancy between the need for new software and the aggregate capacity of the workforce to produce it. This problem has been compounded by the slow growth of programming productivity, especially for high-performance programs, over the past two decades. One way to bridge this gap is to make it possible for end users to develop programs in high-level domain-specific programming systems. In the past, a major impediment to the acceptance of such systems has been the poor performance of the resulting applications. To address this problem, we are developing a new compiler-based infrastructure, called

In this paper we propose an adaptive scheduling approach designed to improve the performance of parallel applications in Computational Grid environments. A primary contribution of our work is that our design is modular and provides a separation of the scheduler itself from the application-specific components needed for the scheduling process. As part of the scheduler, we have also developed a search procedure which effectively and efficiently identifies desirable schedules. As test cases for our approach, we selected two applications from the class of iterative, mesh-based applications. For each of the test applications, we developed data mappers and performance models. We used a prototype of our approach in conjunction with these application-specific components to perform validation experiments in production Grid environments. Our results show that our scheduler provides significantly better application performance than conventional scheduling strategies. We also show that our scheduler gracefully handles degraded levels of availability of application and Grid resource information. Finally, we demonstrate that the overheads introduced by our methodology

Generating high quality schedules for distributed applications on a Computational Grid is a challenging problem.

This paper describes an overall framework for the design of numerical libraries on a computational Grid of processors where the processors may be geographically distributed and under the control of a Grid-based scheduling system. A set of experiments are presented in the context of solving systems of linear equations using routines from the ScaLAPACK software collection along with various grid service components, such as Globus, NWS, and Autopilot. Motivation On The Grid The goal of the Grid Application Development Software (GrADS) project [1] is to simplify distributed heterogeneous computing in the same way that the World Wide Web simplified information sharing over the Internet. The GrADS project is exploring the scientific and technical problems that must be solved to make Grid applications development and performance tuning for real applications an everyday practice. This requires research in four key areas; each validated in a prototype infrastructure that will make programming on grids a routine task: 1. Grid software architectures that facilitate information flow and resource negotiation among applications, libraries, compilers, linkers, and runtime systems; 2. Base software technologies, such as scheduling, resource discovery, and communication, to support development and execution of performance-efficient Grid applications; 3. Languages, compilers, environments, and tools to support creation of applications for the Grid and solution of problems on the Grid; and 4. Mathematical and data structure libraries for Grid applications, including numerical methods for control of accuracy and latency tolerance.