Using idle memory for data-intensive computations (extended abstract)
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Availability and utility of idle memory in workstation clusters
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
General strategies for dynamic reconfiguration
ACM SIGSOFT Software Engineering Notes
Task scheduling performance in distributed systems with time varying workload
Neural, Parallel & Scientific Computations
DyRecT: Software Support for Adaptive Parallelism on NOWs
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
Human exploration and development of space: using XML database space wide web
Information Sciences—Informatics and Computer Science: An International Journal - Internet computing
On/off-line prediction applied to job scheduling on non-dedicated NOWs
Journal of Computer Science and Technology - Special issue on natural language processing
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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 space-sharing 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.