Power-Aware Allocation of Virtual Machine-Based Real-Time Cloudlets in Cloud Data Centers

eman elbedewy, Anas A. Youssef, A. Keshk
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Abstract

Due to the expanding utilization of cloud computing services, power consumption in cloud data centers has increased significantly. The number of active physical hosts impacts data center power usage, so the number of active physical hosts should be decreased. To achieve this goal, cloud data centers use virtualization technology to consolidate multiple virtual machines on a single physical server, using state-of-the-art virtual machine placement algorithms. Specifically, bin packing algorithms have been widely used to place a set of items, i.e., cloudlets and virtual machines, into a set of bins, i.e., virtual machines and physical hosts. However, a set of cloud services, i.e., cloudlets, are characterized as realtime and need to be provided within strict deadlines. In this paper, a cloud resource allocation framework is proposed to provide a compromise between two goals. The proposed framework uses the optimal physical host MIPS to achieve minimum possible power consumption while satisfying virtual machine-based cloudlets' deadline constraints. The proposed framework includes two modules, namely cloudlet allocator and virtual machine allocator. A set of widely used bin packing algorithms is exploited and compared in both modules. Firstly, the algorithms exploited in the cloudlet allocator module include first-fit, best-fit, and round-robin. The evaluation results showed that the round-robin algorithm provides the best outcomes in terms of real-time constraints. Round-robin could allocate an increasing number of cloudlets to virtual machines without scarifying the deadline constraints. Secondly, the algorithms used in the comparison in the virtual machine allocator module include first-fit, best-fit, next-fit, and worst-fit. The results showed that the best-fit algorithm reduces power consumption among all other algorithms under consideration. The results also suggest that setting the physical host CPU MIPS to optimal MIPS achieves the least consumed power. Keywords—Power-Aware Resource Allocation, Cloudlet Allocator, Virtual Machine Allocator, Bin Packing.
云数据中心中基于虚拟机的实时Cloudlets的功耗感知分配
随着云计算服务应用的不断扩大,云数据中心的功耗显著增加。活动物理主机的数量会影响数据中心的电力使用情况,因此应减少活动物理主机的数量。为了实现这一目标,云数据中心使用虚拟化技术在单个物理服务器上整合多个虚拟机,使用最先进的虚拟机放置算法。具体来说,装箱算法已被广泛用于将一组项目(即cloudlets和虚拟机)放入一组箱子(即虚拟机和物理主机)中。但是,一组云服务(即cloudlets)的特点是实时的,需要在严格的期限内提供。在本文中,提出了一个云资源分配框架,以提供两个目标之间的折衷。提出的框架使用最优的物理主机MIPS来实现尽可能小的功耗,同时满足基于虚拟机的cloudlets的截止日期约束。该框架包括两个模块,即cloudlet分配器和虚拟机分配器。在这两个模块中利用并比较了一套广泛使用的装箱算法。首先,在cloudlet分配器模块中使用的算法包括首次拟合、最佳拟合和轮循。评估结果表明,在实时约束条件下,轮循算法提供了最好的结果。循环可以将越来越多的cloudlets分配给虚拟机,而不会影响截止日期限制。其次,在虚拟机分配器模块中,比较使用的算法包括first-fit、best-fit、next-fit和worst-fit。结果表明,在考虑的所有算法中,最优拟合算法的功耗最小。结果还表明,将物理主机CPU MIPS设置为最优MIPS可以实现最少的功耗消耗。关键词:功耗感知资源分配;Cloudlet分配器;虚拟机分配器;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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