Capacity planning and scheduling in Grid computing environments

doi:10.1016/j.future.2007.07.004

2008-05-01

PRE

Ali R. Afzal *

A. Stephen McGough

John Darlington

DOI：10.1016/j.future.2007.07.004

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摘要

En 中文

Grid computing infrastructures embody a cost-effective computing paradigm that virtualises heterogeneous system resources to meet the dynamic needs of critical business and scientific applications. These applications range from batch processes and long-running tasks to real-time and even transactional applications. Grid computing environments are inherently dynamic and unpredictable environments sharing services amongst many different users. Grid schedulers aim to make the most efficient use of Grid resources (high utilisation) while providing the best possible performance to the Grid applications (reducing makespan) and satisfying the associated performance and Quality of Service (QoS) constraints. Additionally, in commercial Grid settings where economic considerations are an increasingly important part of Grid scheduling, it is necessary to minimise the cost of application execution on the behalf of the Grid users while ensuring that the applications meet their QoS constraints. Furthermore, efficient resource allocation may allow a resource broker to maximise their profit by minimising the quantity of resource procurement. Scheduling in such a large-scale, dynamic and distributed environment is a complex undertaking. In this paper, we propose an approach to Grid scheduling which abstracts over the details of individual applications, focusing instead on the global cost optimisation problem while taking into account the entire workload, dynamically adjusting to the varying service demands. Our model places particular emphasis on the stochastic and unpredictable nature of the Grid, leading to a more accurate reflection of the state of the Grid and hence more efficient and accurate scheduling decisions. (c) 2007 Elsevier B.V. All rights reserved.

Keyword:

grid computing

scheduling

brokering

advance reservations

performance

quality-of-service

queueing

optimisation