Abstract not found

The Computational Grid is a promising platform for the efficient execution of parameter sweep applications over large parameter spaces. To achieve performance on the Grid, such applications must be scheduled so that shared data files are strategically placed to maximize reuse, and so that the application execution can adapt to the deliverable performance potential of target heterogeneous, distributed and shared resources. Parameter sweep applications are an important class of applications and would greatly benefit from the development of Grid middleware that embeds a scheduler for performance and targets Grid resources transparently. In this paper we describe a user-level Grid middleware project, the AppLeS Parameter Sweep Template (APST), that uses application-level scheduling techniques [1] and various Grid technologies to allow the efficient deployment of parameter sweep applications over the Grid. We discuss...

this paper, we focus 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 Unix load average, the Unix utility vmstat, and the Network Weather Service 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.

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.

Abstract not found

This paper describes a new technique in the Network Weather Service for producing multi-variate forecasts. The new technique uses the NWS's univariate forecasters and emprically gathered Cumulative Distribution Functions (CDF s) to make predictions from correlated measurement streams. Experimental results are shown in which throughput is predicted for long TCP/IP transfers from short NWS network probes.

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...

In this paper, we discuss requirements for integrating dynamic performance information from the Network Weather Service (NWS) into the Grid Information Service infrastructure (GIS). We describe the object model that NWS uses internally and provide some rationale for its structure. Finally, we present the NWS’s implementation of a caching LDAP daemon that integrates NWS information into the reference GIS – the Globus MDS.

Computation off-loading, i.e., remote execution, has been shown to be effective for extending the computational power and battery life of resource-restricted devices, e.g., hand-held, wearable, and pervasive computers. Remote execution systems must predict the cost of executing both locally and remotely to determine when offloading will be most beneficial. These costs however, are dependent upon the execution behavior of the task being considered and the highly-variable performance of the underlying resources, e.g., CPU (local and remote), bandwidth, and network latency. As such, remote execution systems must employ sophisticated, prediction techniques that accurately guide computation off-loading. Moreover, these techniques must be efficient, i.e., they cannot consume significant resources, e.g., energy, execution time, etc., since they are performed on the mobile device.

A data grid is a collection of geographically dispersed storage resources over wide area network. The goal of data grid system is to provide a large virtual storage framework with unlimited power through collaboration among individuals, institutions, and resources. In this study, we first review a number of data grid services such as, to just name a few, metadata service, data access service, and performance measurement service. We then investigate various techniques for boosting the performance of data grid, and these key techniques have fallen into three camps, namely, data replication, scheduling, and data movement. Since security issue becomes one of the prime concerns in grid computing, we review two interesting issues of security in data grid. Finally and importantly, we have identified five interesting open issues, and pointed out some potential solutions to the open problems.