The interaction of parallel and sequential workloads on a network of workstations
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
The utility of exploiting idle workstations for parallel computation
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Feasibility of a serverless distributed file system deployed on an existing set of desktop PCs
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Resource Management for Rapid Application Turnaround on Enterprise Desktop Grids
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
The Computational and Storage Potential of Volunteer Computing
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Empirical Studies on the Behavior of Resource Availability in Fine-Grained Cycle Sharing Systems
ICPP '06 Proceedings of the 2006 International Conference on Parallel Processing
Scheduling on the Grid via multi-state resource availability prediction
GRID '08 Proceedings of the 2008 9th IEEE/ACM International Conference on Grid Computing
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Parallel computing on volatile distributed resources requires schedulers that consider job and resource characteristics. We study unconventional computing environments containing devices spread throughout a single large organization. The devices are not necessarily typical general purpose machines; instead, they could be processors dedicated to special purpose tasks (for example printing and document processing), but capable of being leveraged for distributed computations. Harvesting their idle cycles can simultaneously help resources cooperate to perform their primary task and enable additional functionality and services. A new burstiness metric characterizes the volatility of the high-priority native tasks. A burstiness-aware scheduling heuristic opportunistically introduces grid jobs (a lower priority workload class) to avoid the higher-priority native applications, and effectively harvests idle cycles. Simulations based on real workload traces indicate that this approach improves makespan by an average of 18.3% over random scheduling, and comes within 7.6% of the theoretical upper bound.