In this paper, we consider the problem of scheduling distributed biological sequence comparison applications. This problem lies in the divisible load framework with negligible communication costs. Thus far, very few results have been proposed in this model. We discuss and select relevant metrics for this framework: namely max-stretch and sumstretch. We explain the relationship between our model and the preemptive uni-processor case, and we show how to extend algorithms that have been proposed in the literature for the uni-processor model to the divisible multi-processor problem domain. We recall known results on closely related problems, derive new lower bounds on the competitive ratio of any on-line algorithm, present new competitiveness results for existing algorithms, and develop several new online heuristics. Then, we extensively study the performance of these algorithms and heuristics in realistic scenarios. Our study shows that all previously proposed guaranteed heuristics for max-stretch for the uni-processor model prove to be particularly inefficient in practice. In contrast, we show our on-line algorithms based on linear programming to be nearoptimal solutions for max-stretch. Our study also clearly suggests heuristics that are efficient for both metrics, although a combined optimization is in theory not possible in the general case.

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.

The Computational Grid has been proposed, for the implementation of high-performance applications using widely dispersed computational resources. The goal of a Computational Grid is to aggregate ensembles of shared, heterogeneous, and distributed resources (potentially controlled by separate organizations) to provide computational, "power" to an application program. We provide a toolkit for the development of globally deployable Grid applications. The toolkit, called EveryWare, enables an application to draw computational power transparently from the Grid. It consists of a portable set of processes and libraries that can be incorporated into an application so that a wide variety of dynamically changing distributed infrastructures and resources can be used together to achieve supercomputer-like performance. We provide our experiences gained while building the EveryWare toolkit prototype and an explanation of its use in implementing a large-scale Grid application.

The computational grid is becoming the platform of choice for large-scale distributed data-intensive applications. Accurately predicting the transfer times of remote data les, a fundamental component of such applications, is critical to achieving application performance. In this paper, we introduce a performance prediction method, ARM (Adaptive Regression Modeling), to determine data transfer times for network-bound distributed dataintensive applications. We demonstrate the eectiveness of the ARM method on two distributed data applications, SARA (Synthetic Aperture Radar Atlas) and SRB (Storage Resource Broker) , and discuss how it can be used for application scheduling. Our experiments demonstrate that applying the ARM method to these applications predicted data transfer times in wide-area multi-user grid environments with accuracy of 88% or better. 1 Introduction Ensembles of distributed computational, storage, and other resources, also known as computational grids [12, 14], are...

The Computational Grid [10] has recently been proposed for the implementation of high-performance applications using widely dispersed computational resources. The goal of a Computational Grid is to aggregate ensembles of shared, heterogeneous, and distributed resources (potentially controlled by separate organizations) to provide computational "power" to an application program. In this paper, we provide a toolkit for the development of Grid applications. The toolkit, called EveryWare, enables an application to draw computational power transparently from the Grid. The toolkit consists of a portable set of processes and libraries that can be incorporated into an application so that a wide variety of dynamically changing distributed infrastructures and resources can be used together to achieve supercomputer-like performance. We provide our experiences gained while building the EveryWare toolkit prototype and the first true Grid application. 1 Introduction Increasingly, the high-perform...