Zhao Jia, Hu Liang, Ding Yan, Xu Gaochao, Hu Ming
College of Computer Science and Engineering, ChangChun University of Technology, Changchun, China; College of Computer Science and Technology, Jilin University, Changchun, China.
College of Computer Science and Technology, Jilin University, Changchun, China.
PLoS One. 2014 Sep 24;9(9):e108275. doi: 10.1371/journal.pone.0108275. eCollection 2014.
The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search's ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable.
实时虚拟机迁移领域一直是绿色云计算中的热点问题。实时虚拟机迁移问题分为两个研究方向：实时虚拟机迁移机制和实时虚拟机迁移策略。同时，随着能量感知计算的发展，我们专注于实时迁移的虚拟机放置选择，即用于节能的实时虚拟机迁移策略。本文提出了一种新颖的启发式方法PS-ES。其主要思想包括两部分。一是将粒子群优化（PSO）思想与模拟退火（SA）思想相结合，以实现一种具有更好全局搜索能力的基于PSO的改进方法。另一个是利用概率论与数理统计，并再次运用SA思想来处理从基于PSO的改进过程中获得的数据，从而得到最终解决方案。因此，整个方法实现了长期节能优化，因为它不仅考虑了当前问题场景的优化，还考虑了未来问题的优化。实验结果表明，与随机迁移和最优迁移相比，PS-ES明显降低了总增量能耗，并更好地保护了虚拟机运行和迁移的性能。结果，所提出的PS-ES方法能够使实时虚拟机迁移事件的结果更高效且有价值。
