Low-Energy-Orientated Resource Scheduling in Cloud Computing by Particle Swarm Optimization

基于粒子群优化的云计算低能耗资源调度算法

School of Automation, Northwestern Polytechnical University, Xi'an 710072, China

Received: 12 June 2017

Abstract

In order to reduce the energy cost in cloud computing, this paper represents a novel energy-orientated resource scheduling method based on particle swarm optimization. The energy cost model in cloud computing environment is studied first. The optimization of energy cost is then considered as a multiobjective optimization problem, which generates the Pareto optimization set. To solve this multiobjective optimization problem, the particle swarm optimization is involved. The states of one particle consist of both the allocation plan for servers and the frequency plans on servers. Each particle in this algorithm obtains its Pareto local optimization. After the assembly of local optimizations, the algorithm generates the Pareto global optimization for one server plan. The final solution to our problem is the optimal one among all server plans. Experimental results show the good performance of the proposed method. Comparing with the widely-used Round robin scheduling method, the proposed method requires only 45.5% dynamic energy cost.

摘要

针对云计算环境中能耗过高问题，提出一种基于粒子群优化方法的云计算低能耗资源调度算法。首先建立了云环境中资源调度的能耗模型；在此模型基础上，指出能耗最优是多目标优化的帕累托（Pareto）最优问题。根据能耗模型，将粒子参数设为服务器分配状态和频率分配状态，从而寻找获得单粒子的局部最优帕累托解集；合并多个粒子最优解集，得到单个分配方案下帕累托全局最优解（Pareto optimality）集合；最后，在不同分配方案对应的最优解集合中寻找最优解。实验验证了所提算法的有效性。与广泛使用的轮询调度算法比较，所提算法的动态能耗为轮询算法的45.5%。