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. We discuss the AppLeS project and outline our findings.

Porting large applications to distributed computing platforms is a challenging task from a software engineering perspective. The Computational Grid has gained tremendous popularity as it aggregates unprecedented amounts of compute and storage resources by means of increasingly high performance network technology. The primary aim of this paper is to demonstrate how the development time to port very large applications to this environment can be significantly reduced. TOP-C and AMPIC are software packages that have each seen successful application in their respective domains of parallel computing and process creation/communication. We combine them to implement and deploy a master-worker model of parallel computing over the Computational Grid. To demonstrate the benefit of our approach, we ported the 1,000,000 line Geant4 sequential code in three man-weeks by using our TOP-C/AMPIC integration. This paper evaluates the benefits of our approach from a software engineering perspective, and presents experimental results obtained with the new implementation of Geant4 on a Grid testbed.

Interactive program analysis tools are often tailored to one particular representation of programs, making adaptation to a new language costly. One way to ease adaptability is to introduce an intermediate abstraction—an adaptation layer—between an existing language representation and the program analysis tool. This adaptation layer translates the tool's queries into queries on the particular representation.

Focuses on the problem of making short- and medium-term forecasts of CPU availability on time-shared Unix systems. We evaluate the accuracy with which availability can be measured using the Unix load average, the Unix utility "vmstat" and the Network Weather Service (NWS) CPU sensor that uses both. We also examine the autocorrelation between successive CPU measurements to determine their degree of self-similarity. While our observations show a long-range autocorrelation dependence, we demonstrate how this dependence manifests itself in the short- and medium-term predictability of the CPU resources in our study.