Making the Grid Predictable through Reservations and Performance Modelling

Authors:
Andrew Stephen Mcgough;Ali Afzal;John Darlington;Nathalie Furmento;Anthony Mayer;Laurie Young
Affiliations:
London e-Science Centre, Imperial College London South Kensington Campus, SW7 2AZ, UK;London e-Science Centre, Imperial College London South Kensington Campus, SW7 2AZ, UK;London e-Science Centre, Imperial College London South Kensington Campus, SW7 2AZ, UK;London e-Science Centre, Imperial College London South Kensington Campus, SW7 2AZ, UK;London e-Science Centre, Imperial College London South Kensington Campus, SW7 2AZ, UK;London e-Science Centre, Imperial College London South Kensington Campus, SW7 2AZ, UK
Venue:
The Computer Journal
Year:
2005

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Abstract

Unpredictable job execution environments pose a significant barrier to the widespread adoption of the Grid paradigm, because of the innate risk of jobs failing to execute at the time specified by the user. We demonstrate that predictability can be enhanced with a supporting infrastructure consisting of three parts: Performance modelling and monitoring, scheduling which exploits application structure and an advanced reservation resource management service. We prove theoretically that execution times using advanced reservations display less variance than those without. We also show that the costs of advanced reservations can be reduced by providing the system with more accurate performance models. Following the theoretical discussion, we describe the implementation of a fully functional workflow enactment framework that supports advanced reservations and performance modelling thereby providing predictable execution behavior. We further provide experimental results confirming our theoretical models.