Future Generation Computer Systems - Special issue on metacomputing
ARIMA time series modeling and forecasting for adaptive I/O prefetching
ICS '01 Proceedings of the 15th international conference on Supercomputing
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Toward a Framework for Preparing and Executing Adaptive Grid Programs
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Prophesy: an infrastructure for performance analysis and modeling of parallel and grid applications
ACM SIGMETRICS Performance Evaluation Review
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
A framework for adaptive execution in grids
Software—Practice & Experience
Performance control of scientific coupled models in Grid environments: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Self adaptivity in Grid computing: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Using Regression Techniques to Predict Large Data Transfers
International Journal of High Performance Computing Applications
GCF: a general coupling framework
Concurrency and Computation: Practice & Experience - Computational Frameworks
IEEE Transactions on Parallel and Distributed Systems
Time series models for internet traffic
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
ScoPred–scalable user-directed performance prediction using complexity modeling and historical data
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
Adaptive execution of jobs in computational grid environment
Journal of Computer Science and Technology - Special section on trust and reputation management in future computing systmes and applications
Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster Grids
Journal of Grid Computing
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The PerCo performance control framework is capable of managing the distributed execution of scientific coupled models using migration, for example, in response to changes in an execution environment. PerCo monitors execution times and reacts according to an adaptive performance control strategy whenever serious changes of behaviour occur. A computationally cheap technique is used per model to smooth the series of monitored execution times and to provide a short-term forecast for future execution times on currently assigned resources. Where this short-term forecast fails to be achieved, the system analyses whether migration would improve matters. For models that are candidates for migration, more accurate but computationally expensive techniques are used to form a longer-term prediction of future execution times on various candidate resources. Based on the predicted gain, a migration decision is made taking account of the expected cost of migration. Experimental results for small real scientific coupled models show that the performance control strategy behaves effectively in scenarios in which the ambient load is varied during execution.