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SETI@home: an experiment in public-resource computing
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HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
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HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
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Adaptive Task Checkpointing and Replication: Toward Efficient Fault-Tolerant Grids
IEEE Transactions on Parallel and Distributed Systems
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Future Generation Computer Systems
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WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Peer-to-peer service provisioning in cloud computing environments
The Journal of Supercomputing
An approximate ϵ-constraint method for a multi-objective job scheduling in the cloud
Future Generation Computer Systems
Maximizing stochastic robustness of static resource allocations in a periodic sensor driven cluster
Future Generation Computer Systems
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A major performance issue in large-scale decentralized distributed systems, such as grids, is how to ensure that jobs finish their execution within the estimated completion times in the presence of resource performance fluctuations. Previously, several techniques including advance reservation, rescheduling and migration have been adopted to resolve/relieve this issue; however, they have some non-negligent practicality hurdles. The use of clouds may be an attractive alternative, since resources in clouds are much more reliable than those in grids. This paper investigates the effectiveness of rescheduling using cloud resources to increase the reliability of job completion. Specifically, schedules are initially generated using grid resources, and cloud resources (relatively costlier) are used only for rescheduling to cope with a delay in job completion. A job in our study refers to a bag-of-tasks (BoT) application that consists of a large number of independent tasks; this job model is common in many science and engineering applications. We have devised a novel rescheduling technique, called rescheduling using clouds for reliable completion (RC^2) and applied it to three well-known existing heuristics. Our experimental results reveal that RC^2 significantly reduces delay in job completion.