Scheduling Workflow in Cloud Computing Based on Hybrid Particle Swarm Algorithm

Sheng-Jun Xue, Wu Wu


The cost minimization with due dates in cloud computing workflow is an intractable problem. Taking the characteristics in cloud computing of pay-per-use and resource virtualization into account, in this paper, we present a QoS-based hybrid particle swarm optimization (GHPSO) to schedule applications to cloud resources. In GHPSO, crossover and mutation of genetic algorithm is embedded into the particle swarm optimization algorithm (PSO), so that it can play a role in the discrete problem, in addition, variability index, changing with the number of iterations, is proposed to ensure that population can have higher global search ability during the early stage of evolution, without the premature phenomenon. A hill climbing algorithm is also introduced into the PSO in order to improve the local search ability and to maintain the diversity of the population. The simulation results show that the GHPSO achieves better performance than standard particle swarm algorithm used in minimize costs within a given execution time.



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