论文标题
大规模虚拟机放置在云计算中的多因素优化
Multi-factorial Optimization for Large-scale Virtual Machine Placement in Cloud Computing
论文作者
论文摘要
虚拟机（VM）对物理服务器（PSS）的放置方案对于降低云提供商的运营成本至关重要。过去，在虚拟机放置（VMP）问题上，进化算法（EAS）已进行了有希望的解决。但是，随着对云服务需求不断增长，由于高度复杂性和较差的可扩展性，现有的EAS无法在大规模虚拟机放置（LVMP）问题中实施。最近，多因素优化（MFO）技术已作为进化计算中的新搜索范式浮出水面。它提供了在进化过程中同时发展多个优化任务的能力。本文旨在将MFO技术应用于异质环境中的LVMP问题。首先，我们以MFO问题的形式制定了基于部署成本的VMP问题。然后，开发了一种嵌入基于贪婪的分配操作员的多因素进化算法（MFEA），以解决已建立的MFO问题。之后，重新迁移和合并操作员旨在从MFO问题解决方案中提供LVMP问题的集成解决方案。为了评估我们提出的方法的有效性，模拟实验是在大规模和大型VMS测试数据集上进行的。结果表明，与各种启发式方法相比，我们的方法可以显着缩短优化时间，并为在异质环境中的LVMP问题提供竞争性的位置解决方案。
The placement scheme of virtual machines (VMs) to physical servers (PSs) is crucial to lowering operational cost for cloud providers. Evolutionary algorithms (EAs) have been performed promising-solving on virtual machine placement (VMP) problems in the past. However, as growing demand for cloud services, the existing EAs fail to implement in large-scale virtual machine placement (LVMP) problem due to the high time complexity and poor scalability. Recently, the multi-factorial optimization (MFO) technology has surfaced as a new search paradigm in evolutionary computing. It offers the ability to evolve multiple optimization tasks simultaneously during the evolutionary process. This paper aims to apply the MFO technology to the LVMP problem in heterogeneous environment. Firstly, we formulate a deployment cost based VMP problem in the form of the MFO problem. Then, a multi-factorial evolutionary algorithm (MFEA) embedded with greedy-based allocation operator is developed to address the established MFO problem. After that, a re-migration and merge operator is designed to offer the integrated solution of the LVMP problem from the solutions of MFO problem. To assess the effectiveness of our proposed method, the simulation experiments are carried on large-scale and extra large-scale VMs test data sets. The results show that compared with various heuristic methods, our method could shorten optimization time significantly and offer a competitive placement solution for the LVMP problem in heterogeneous environment.
