The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
A Slowdown Model for Applications Executing on Time-Shared Clusters of Workstations
IEEE Transactions on Parallel and Distributed Systems
Impact of Workload and System Parameters on Next Generation Cluster Scheduling Mechanisms
IEEE Transactions on Parallel and Distributed Systems
An Efficient Adaptive Scheduling Scheme for Distributed Memory Multicomputers
IEEE Transactions on Parallel and Distributed Systems
High Performance Cluster Computing: Programming and Applications
High Performance Cluster Computing: Programming and Applications
GRID '02 Proceedings of the Third International Workshop on Grid Computing
Stochastic Prediction of Execution Time for Dynamic Bulk Synchronous Computations
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Task Execution Time Modeling for Heterogeneous Computing Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Modeling and characterizing parallel computing performance on heterogeneous networks of workstations
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Modeling Parallel Applications Performance on Heterogeneous Systems
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Journal of Systems and Software
The impact of channel variations on wireless distributed computing networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Performance evaluation of bag of gangs scheduling in a heterogeneous distributed system
Journal of Systems and Software
Gang scheduling in multi-core clusters implementing migrations
Future Generation Computer Systems
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One of the distinct characteristics of computing platforms shared by multiple users such as a cluster and a computational grid is heterogeneity on each computer and/or among computers. Temporal heterogeneity refers to variation, along the time dimension, of computing power available for a task on a computer, and spatial heterogeneity represents the variation among computers. In minimizing the average parallel execution time of a target task on a spatially heterogeneous computing system, it is not optimal to distribute the target task linearly proportional to the average computing powers available on computers. In this paper, effects of the temporal and spatial heterogeneity on performance of a target task have been analyzed in terms of the mean and standard deviation of parallel execution time. Based on the analysis results, an approach to load balancing for minimizing the average parallel execution time of a target task is described. The proposed approach whose validity has been verified through simulation considers temporal and spatial heterogeneities in addition to the average computing power on each computer.