Utopia: a load sharing facility for large, heterogeneous distributed computer systems
Software—Practice & Experience
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Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
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IEEE Transactions on Parallel and Distributed Systems
Performance Contracts: Predicting and Monitoring Grid Application Behavior
GRID '01 Proceedings of the Second International Workshop on Grid Computing
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IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
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HICSS '96 Proceedings of the 29th Hawaii International Conference on System Sciences Volume 1: Software Technology and Architecture
Autopilot: Adaptive Control of Distributed Applications
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
Feedback Control Scheduling in Distributed Real-Time Systems
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
An Introduction to the Kalman Filter
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PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Feedback Utilization Control in Distributed Real-Time Systems with End-to-End Tasks
IEEE Transactions on Parallel and Distributed Systems
Backfilling Using System-Generated Predictions Rather than User Runtime Estimates
IEEE Transactions on Parallel and Distributed Systems
Concurrency and Computation: Practice & Experience - European–American Working Group on Automatic Performance Analysis (APART)
Automatic resource specification generation for resource selection
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
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HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
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Performance prediction based resource selection in grid environments
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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A key problem in executing performance critical applications on distributed computing environments (e.g. the Grid) is the selection of resources. Research related to "automatic resource selection" aims to allocate resources on behalf of users to optimize the execution performance. However, most of current approaches are based on the static principle (i.e. resource selection is performed prior to execution) and need detailed application-specific information. In the paper, we introduce a novel on-line automatic resource selection approach. This approach is based on a simple control theory: the application continuously reports the Execution Satisfaction Degree (ESD) to the middleware Application Agent (AA), which relies on the reported ESD values to learn the execution behavior and tune the computing environment by adding/replacing/deleting resources during the execution in order to satisfy users' performance requirements. We introduce two different policies applied to this approach to enable the AA to learn and tune the computing environment: the Utility Classification policy and the Desired Processing Power Estimation (DPPE) policy. Each policy is validated by an iterative application and a non-iterative application to demonstrate that both policies are effective to support most kinds of applications.