Abstract

Bandwidth-aware workflow scheduling is required to improve the performance of a workflow application in a multisite Grid environment, as the data movement cost between two low-bandwidth sites can adversely affect the makespan of the application. Pegasus WMS, an open-source and freely available WMS, cannot fully utilize its workflow mapping capability due to unavailability of integration of any bandwidth monitoring infrastructure in it. This paper develops the integration of Network Weather Service (NWS) in Pegasus WMS to enable the bandwidth-aware mapping of scientific workflows. Our work demonstrates the applicability of the integration of NWS by making existing Heft site-selector of Pegasus WMS bandwidth aware. Furthermore, this paper proposes and implements a new workflow scheduling algorithm—Level based Highest Input and Processing Weight First. The results of the performed experiments indicate that the bandwidth-aware workflow scheduling algorithms perform better than bandwidth-unaware algorithms: Random and Heft of Pegasus WMS. Moreover, our proposed workflow scheduling algorithm performs better than the bandwidth-aware Heft algorithms. Thus, the proposed bandwidth-aware workflow scheduling enhances capability of Pegasus WMS and can increase performance of workflow applications.

Highlights

  • Grid computing [1] enables executing performance demanding applications efficiently by exploiting distributed resources in a collaborative manner [2]

  • We present an example of NwsAwareHeft, an extension of the Heft algorithm of Pegasus WMS, in Listing 1 to explain how bandwidth-aware workflow scheduling is embedded in Pegasus WMS

  • The results show that bandwidthaware workflow scheduling algorithms BW-Aware-Heft, BWAware-original-Heft, and BW-Aware-LHIPWF spend less time in performing data communication as compared to bandwidth-unaware algorithms: Random and Heft

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Summary

Introduction

Grid computing [1] enables executing performance demanding applications efficiently by exploiting distributed resources in a collaborative manner [2]. A few examples that include LIGO [3], Montage [4], BLAST [5], and WIEN2K [6], try to solve their computing problems by making computation demanding applications composed of reusable executables. Various systems such as Pegasus WMS [7], Askalon [8], Kepler [9], Karajan [10], Taverna [11], and Triana [12] have been used by such projects to execute computation demanding applications. Journal of Computer Networks and Communications the workflow scheduling capability of Pegasus WMS remains underutilized

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