Abstract
Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.
Highlights
Cloud computing is a pay-as-you-go model which provides resources at lower costs with greater reliability and delivers the resources by means of virtualization technologies [1]
We present scheduling based on Chaotic Particle Swarm Optimization (CPSO) to tackle this problem
In order to properly control the impact of previous velocity, a suitable adaptive inertia weight factor is applied into CPSO
Summary
Cloud computing is a pay-as-you-go model which provides resources at lower costs with greater reliability and delivers the resources by means of virtualization technologies [1]. The performance of GA, ACO, and PSO mostly depends on its parameters, and it has the characteristic of being trapped in local optima In a word, these algorithms cannot achieve the optimal cost of scheduling. The CPSO-based scheduling algorithm aims at minimizing the cost of the workflow system. The objective of security-constrained unit commitment is to minimize the total generation cost, which is the total of both transition cost and production cost of the scheduled units These researches adopt chaotic sequence instead of random sequence in PSO to improve the efficiency of the algorithm. In order to properly control the impact of previous velocity, a suitable adaptive inertia weight factor is applied into CPSO This weight factor depends on the optimization value of fitness calculation. The adaptive inertia weight factor provides a good way to preserve diversity of population and maintain good convergence
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