Procedia Computer Science · 2015 · 141 citations · 9 references
ComputingCloud ComputingComputer ScienceCloud Performance OptimizationEnergy ManagementHeterogeneous ComputingCloud Resource ManagementCompute Resource ManagementEnergy-efficient Cloud ComputingMulti-objective SchedulingResource SchedulingTask SchedulingCloud SchedulingComputational ArchitectureEnergy-aware Resource SchedulingEnergy-efficient Resource ManagementMultiobjective Particle Swarm OptimizationParticle Swarm OptimizationPower ConsumptionPower Consumption Optimization
Cloud computing is an emerging computing paradigm with a large collection of heterogeneous autonomous systems with flexible computational architecture. Task scheduling is an important step to improve the overall performance of the cloud computing. Task scheduling is also essential to reduce power consumption and improve the profit of service providers by reducing processing time. This paper focuses on task scheduling using a multi-objective nested Particle Swarm Optimization(TSPSO) to optimize energy and processing time. The result obtained by TSPSO was simulated by an open source cloud platform (CloudSim). Finally, the results were compared to existing scheduling algorithms and found that the proposed algorithm (TSPSO) provide an optimal balance results for multiple objectives.
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