Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Divisible task scheduling — concept and verification
Parallel Computing - Special issue on task scheduling problems for parallel and distributed systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Scheduling Divisible Loads in Parallel and Distributed Systems
Scheduling Divisible Loads in Parallel and Distributed Systems
Access Time Minimization for Distributed Multimedia Applications
Multimedia Tools and Applications
Experiments with Scheduling Divisible Tasks in Clusters of Workstations
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
Out-of-core divisible load processing
IEEE Transactions on Parallel and Distributed Systems
Performance-based workload distribution on grid environments
GPC'07 Proceedings of the 2nd international conference on Advances in grid and pervasive computing
GPC'06 Proceedings of the First international conference on Advances in Grid and Pervasive Computing
GPC'06 Proceedings of the First international conference on Advances in Grid and Pervasive Computing
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
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In this paper we study multi-installment divisible load processing in heterogeneous distributed systems. Divisible loads are computations which can be divided into parts of arbitrary sizes, and these parts can be processed independently in parallel. In order to reduce the waiting time during the parallel computation initialization phase, load is sent to the processors in multiple small installments. In a heterogeneous system the sizes of the installments should be adjusted to the communication, and computation capabilities of the processors. We propose two algorithms that gear the load chunk sizes to different communication and computation speeds. The first one is an optimization branch and bound algorithm. The second algorithm is based on genetic search. The running times of both methods and the quality of the genetic algorithm solutions are compared. Then, we use these algorithms to analyze features of the scheduling problem solutions.