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VIRTUAL MACHINE DEPLOYMENT STRATEGY BASED ON IMPROVED PSO IN CLOUD COMPUTING

机译:基于改进PSO在云计算的虚拟机部署策略

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Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.
机译:能源消耗是由计算能力的增长驱动的重要成本,从而节能已成为云系统面临的主要问题之一。如何最大限度地提高物理机器的利用率,减少虚拟机迁移的数量,并在物理机资源阈值的约束下维护负载余额,这是实现数据中心中节能的有效方法。在论文中,我们提出了一种用于虚拟机部署的多目标物理模型。然后将改进的多目标粒子群优化(TPSO)应用于虚拟机部署。与其他算法相比,该算法具有更好的遍历进入初始阶段,提高了粒子群的优化精度和优化效率。基于CloudSim仿真平台的实验结果表明,该算法在提高物理机资源利用率,减少资源浪费和改善系统负载平衡方面有效。

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