Clusters of workstations are increasingly being viewed as a cost-effective alternative to parallel supercomputers. However, resource management and scheduling on workstations clusters is complicated by the fact that the number of idle workstations available for executing parallel applications is constantly fluctuating. In this paper, we present a case for scheduling parallel applications on non-dedicated workstation clusters using dynamic space-sharing, a policy under which the number of processors allocated to an application can be changed during its execution. We describe an approach that uses application-level checkpointing and data repartitioning for supporting dynamic spacesharing and for handling the dynamic reconfiguration triggered when failure or owner activity is detected on a workstation being used by a parallel application. The performance advantages of dynamic space-sharing are quantified through a simulation study, and experimental results are presented for the overhead of dynamic reconfiguration of a grid-oriented data parallel application using our approach.
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