Multi-criteria genetic algorithm applied to scheduling in multi-cluster environments
| Autor: | Josep L. Lérida, Eloi Gabaldon, Jordi Planes, Fernando Guirado |
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| Rok vydání: | 2015 |
| Předmět: |
Multi-cluster
020203 distributed computing education.field_of_study Mathematical optimization 021103 operations research Job shop scheduling Linear programming Scheduling Computer science Quality of service Real-time computing Population 0211 other engineering and technologies 02 engineering and technology Genetic algorithms Scheduling (computing) Modelling and Simulation Modeling and Simulation Genetic algorithm 0202 electrical engineering electronic engineering information engineering Discrete event simulation Heuristics education Co-allocation Software |
| Zdroj: | Repositorio Abierto de la UdL Universitad de Lleida Recercat. Dipósit de la Recerca de Catalunya instname |
| ISSN: | 1747-7786 1747-7778 |
| DOI: | 10.1057/jos.2014.41 |
| Popis: | Scheduling and resource allocation to optimize performance criteria in multi-cluster heterogeneous environments is known as an NP-hard problem, not only for the resource heterogeneity, but also for the possibility of applying co-allocation to take advantage of idle resources across clusters. A common practice is to use basic heuristics to attempt to optimize some performance criteria by treating the jobs in the waiting queue individually. More recent works proposed new optimization strategies based on Linear Programming techniques dealing with the scheduling of multiple jobs simultaneously. However, the time cost of these techniques makes them impractical for large-scale environments. Population-based meta-heuristics have proved their effectiveness for finding the optimal schedules in large-scale distributed environments with high resource diversification and large numbers of jobs in the batches. The algorithm proposed in the present work packages the jobs in the batch to obtain better optimization opportunities. It includes a multi-objective function to optimize not only the Makespan of the batches but also the Flowtime, thus ensuring a certain level of QoS from the users’ point of view. The algorithm also incorporates heterogeneity and bandwidth awareness issues, and is useful for scheduling jobs in large-scale heterogeneous environments. The proposed meta-heuristic was evaluated with a real workload trace. The results show the effectiveness of the proposed method, providing solutions that improve the performance with respect to other well-known techniques in the literature. This work was supported by the Government of Spain under contract TIN2011-28689-C02-02 and the CUR of DIUE of GENCAT and the European Social Fund. |
| Databáze: | OpenAIRE |
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