Ensembles of distributed, heterogeneous resources, also known as Computational Grids, have emerged as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in heterogeneous, multiuser grid environments. In this article, we discuss the AppLeS project and outline our findings.

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Like all computing platforms, grids are in need of a suite of benchmarks by which they can be evaluated and characterized. As a first step towards this goal, we have developed a set of probes that exercise basic grid operations by simulating simple grid applications. We run these probes on a testbed, collecting performance data such as compute times, network transfer times, and Globus middleware overhead. The results of our experiments help provide insight into the stability, robustness, and performance of this grid testbed, as well as some recommendations for future grid development.