A Distributed Drafting Algorithm for Load Balancing
IEEE Transactions on Software Engineering
Cooperative distributed dynamic load balancing
Acta Informatica
Heuristic methods for dynamic load balancing in a message-passing multicomputer
Journal of Parallel and Distributed Computing
On Process Migration and Load Balancing in Time Warp
IEEE Transactions on Parallel and Distributed Systems
An Application of Bayesian Decision Theory to Decentralized Control of Job Scheduling
IEEE Transactions on Computers
Load Sharing in Distributed Systems
IEEE Transactions on Computers
What is worth learning from parallel workloads?: a user and session based analysis
Proceedings of the 19th annual international conference on Supercomputing
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The static task allocation problem and the dynamic load balancing problem are relevant issues when using a multiprocessor computer system to execute parallelized tasks. The paper presents an artificial intelligence (AI) strategy to resolve the above problems under the PVM (Parallel Virtual Machine). The AI technique is employed to accurately predict better allocation for PVM tasks in their initial assignment as well as execution time. The performance characteristics of the proposed load balancing algorithm are also examined for a homogeneously distributed system. Three different problems are implemented for evaluating our algorithm's performance. Experimental results demonstrate that our algorithm performs more effectively than conventional approaches.