Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms
Artificial Intelligence Review
PGGA: a predictable and grouped genetic algorithm for job scheduling
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
A decentralized strategy for genetic scheduling in heterogeneous environments
Multiagent and Grid Systems - Grid Computing, high performance and distributed applications
PGGA: A predictable and grouped genetic algorithm for job scheduling
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
A genetic algorithm for job shop scheduling with load balancing
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A decentralized strategy for genetic scheduling in heterogeneous environments
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part II
Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm
Journal of Grid Computing
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We produce a GA scheduling routine, which with often relatively low cost finds well-balanced schedules. Incoming tasks (of varying durations) accumulate, then are periodically scheduled, in small batches, to the available processors. Two important priorities for our scheduling work are that loads on the processors are well balanced, and that scheduling per se remains cheap in comparison to the actual productive work of the processors. We also include experimental results, exploring a variety of distributions of task durations, which show that our scheduler consistently produces well-balanced schedules, and quite often does so at relatively low cost.