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• W2604554343 abstract "A key challenge faced by large-scale computing platforms to go green is the effective utilization of energy at the various processing nodes. Most existing scheduling models assume that processors are able to stay online forever. In reality, processors, however, may have arbitrary unavailable time periods. Hence, if we inadvertently assign tasks to processors without considering the availability constraints, some processors would not be able to finish their assigned workloads. Thus all the unfinished workloads need to be reassigned to other available processors resulting in an inefficient time and energy schedule. In this chapter, we propose a novel processor availability-aware divisible-load scheduling model. Using this model, we design a time-efficient genetic algorithm based global optimization technique to derive an optimal load distribution strategy. Our experimental results show that the proposed algorithm adapts to minimize the processing time, hence the energy consumption too, by over 60% compared to other strategies." @default.
• W2604554343 created "2017-04-14" @default.
• W2604554343 creator A5058603157 @default.
• W2604554343 creator A5070594442 @default.
• W2604554343 date "2017-01-01" @default.
• W2604554343 modified "2023-09-27" @default.
• W2604554343 title "A Genetic Algorithm Based Efficient Static Load Distribution Strategy for Handling Large-Scale Workloads on Sustainable Computing Systems" @default.
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• W2604554343 doi "https://doi.org/10.1007/978-3-319-53153-3_2" @default.
• W2604554343 hasPublicationYear "2017" @default.
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