Managing Uncertainty: A Case for Probabilistic Grid Scheduling
Aleksandar Lazarevic (University College London)Lionel Sacks (University College London)
Ognjen Prnjat (University College London)
Abstract:
The Grid technology is evolving into a global, service-orientated architecture – a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of delivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job’s historical executions and its associated meta-data. This approach is motivated by a study of a six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture.
Keywords:
Cluster and Grid Computing, Parallel and Distributed Computing
