>In current era, the trend of cloud computing is increasing with every passing day due'/> Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment
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Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment

机译:基于经典bin pack和遗传算法的云环境虚拟机部署性能评估

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>In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.
机译: >在当前时代,由于云计算的一项主要服务,即基础架构即服务(IAAS),云计算的趋势每天都在增加,该服务通过在单个物理机上创建多个VM实例来虚拟化硬件。对硬件进行虚拟化可以提高资源利用率,但同时也会导致系统利用率过高而性能低下。因此,需要使用VM合并过程将这些VM迁移到另一台物理计算机,以减少主机数量并提高系统性能。因此,将虚拟机放置在其他主机上的想法导致提出了许多新的VM放置算法。但是,减少的物理机集需要较少的功耗。在当前的工作中,作者提出了一种基于遗传算法的决策VM放置系统,并将其与基于经典bin打包的三种预定义VM放置技术进行了比较。这种分析有助于更好地了解放置策略对云环境整体性能的影响,以及与传统的bin打包算法相比,遗传算法的使用如何为VM放置提供更好的结果。

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