Leveraging content similarity among VMI files to allocate virtual machines in cloud
Leveraging content similarity among VMI files to allocate virtual machines in cloud
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DOI：
10.1016/j.future.2017.09.058
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Huixi Li;Wen J. Li;Qilong Feng;Shigeng Zhang;Haodong Wang;Jianxin Wang
中科院分区：
文献类型：
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作者：
Huixi Li;Wen J. Li;Qilong Feng;Shigeng Zhang;Haodong Wang;Jianxin Wang
为了满足大量客户的需求，必须在云数据中心同时配置大量虚拟机 (VM)。由于需要通过网络传输的虚拟机映像 (VMI) 文件很大，因此配置通常非常耗时。为了解决这个问题，研究人员尝试利用不同 VMI 文件之间的内容相似性来减少传输的数据量。在虚拟机配置中，最小化物理机数量的虚拟机打包问题是另一个重要问题。在本文中，我们的目标是找到一种解决方案，尝试将 VM 打包到最少的 PM 数量，并显着减少传输的数据总量。我们正式定义了VM打包和最小化VM配置中的数据传输问题，命名为RTVD-VA。我们首先提出了一种近似算法，以在向单个物理机提供相同大小的 K 个虚拟机时最小化传输的 VMI 数据量。然后，我们扩展算法以解决使用最小数量 PM 时存在多个 PM 的情况。基于上述两种近似算法，我们提出了一种启发式算法，即Balance-Placement，来解决一般情况下的问题。我们的模拟结果表明，Balance-Placement 优于 PSO 和 Greedy-Cache 等现有解决方案，并且在大多数场景下实现了最少的传输数据量和最少的 PM 使用数量。
To meet a myriad of customers’ demands, a large number of virtual machines (VMs) have to be provisioned simultaneously in cloud data centers. Provisioning is usually time consuming due to the large size of virtual machine image (VMI) file that needs to be transferred via networks. To address this issue, researchers attempt to leverage the content similarity among different VMI files to reduce the volume of transferred data. In the VM provisioning, the VM packing problem that minimizes the number of physical machines is another important issue. In this paper, our goal is to find a solution that tries to pack VMs to the minimum number of PMs as well as significantly reduces the total amount of transferred data. We formally define the problem of VM packing and minimizing the data transferring in the VM provisioning, named RTVD-VA. We first propose an approximation algorithm to minimize the amount of transferred VMI data when provisioning K VMs with the same size to a single physical machine. We then extend the algorithm to address the scenario of multiple PMs when using the minimum number of PMs. Based on the above two approximation algorithms, we propose a heuristic algorithm, namely Balance-Placement, to solve the problem in general cases. Our simulation results show that Balance-Placement outperforms existing solutions like PSO and Greedy-Cache and achieves the least amount of transferred data and the minimum number of used PMs in most scenarios.