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SIAM Journal on Computing
Space-efficient scheduling of multithreaded computations
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Exploiting process lifetime distributions for dynamic load balancing
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STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
LogP: a practical model of parallel computation
Communications of the ACM
On optimal strategies for cycle-stealing in networks of workstations
IEEE Transactions on Software Engineering
In search of clusters (2nd ed.)
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Load-balancing heuristics and process behavior
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A Case for NOW (Networks of Workstations)
IEEE Micro
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
The Computational Co-op: Gathering Clusters into a Metacomputer
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Sharing Partitionable Workloads in Heterogeneous NOWs: Greedier Is Not Better
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid
Future Generation Computer Systems - Special issue: Advanced grid technologies
Static worksharing strategies for heterogeneous computers with unrecoverable failures
Euro-Par'09 Proceedings of the 2009 international conference on Parallel processing
Improving speedup and response times by replicating parallel programs on a SNOW
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
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We refine the model underlying our prior work on scheduling bag-of-tasks (驴embarrassingly parallel驴) workloads via cycle-stealing in networks of workstations obtaining a model wherein the scheduling guidelines ofproduce optimal schedules for every such cycle-stealing opportunity. We thereby render prescriptive the descriptive model of those sources. Although computing optimal schedules usually requires the use of general function-optimizing methods, we show how to compute optimal schedules efficiently for the broad class of opportunities whose durations come from a concave probability distribution. Even when no such efficient computation of an optimal schedule is available, our refined model often suggests a natural notion of approximately optimal schedule, which may be efficiently computable. We illustrate such efficient approximability via the important class of cycle-stealing opportunities whose durations come from a heavy-tailed distribution. Such opportunities do not admit any optimal schedule驴nor even a natural notion of approximately optimal schedule驴within the model.Within our refined model, though, we derive computationally simple schedules for heavy-tailed opportunities, which can be 驴tuned驴 to accomplish an expected amount of work that is arbitrarily close to optimal.