Cloud computing provides utility computing in which clients pay the cost according to their demands and service use. There are some challenges to this technology. One of these issues in data centers is virtual machine (VM) placement so that mapping of these VMs to hosts is executed for a variety of objectives such as load balancing, reducing energy consumption, increasing resource utilization, shortening response time, etc. In this paper, a strategy is presented based on machine learning for VM replacement which aims to balance the load in host machines (HM). In this proposed strategy, the learning agent, in each learning episode by selecting an action from among the permissible actions and executing it on the environment receives a reward according to the desirability of the solution obtained by doing that action in the environment. Receiving a reward from the environment and updating the action value table enable the learner agent to learn in the following episodes that in each environment state, selecting and executing which action is better in the environment and this leads to further enhancement. Our proposed algorithm has, on average, improved the inter-HM load balance in terms of processor, memory, and bandwidth by 25%, 34%, and 32%, respectively, prior to the implementation of the algorithm. Our strategy was compared from diffrent aspects in three scenarios to the MOVMrB strategy. Finally, it was concluded that our proposed algorithm can be more effective in load balancing by having much less runtime and turning off more HMs.

中文翻译：

---

基于机器学习的云数据中心虚拟机放置多目标负载均衡算法

云计算提供效用计算，客户根据他们的需求和服务使用支付费用。这项技术存在一些挑战。数据中心中的这些问题之一是虚拟机 (VM) 放置，以便将这些 VM 映射到主机以实现各种目标，例如负载平衡、降低能耗、提高资源利用率、缩短响应时间等。论文中，提出了一种基于机器学习的 VM 替换策略，旨在平衡主机 (HM) 中的负载。在这个提议的策略中，学习代理在每个学习阶段通过从允许的动作中选择一个动作并在环境上执行它，根据通过在环境中执行该动作获得的解决方案的可取性来获得奖励。从环境中接收奖励并更新动作值表使学习者代理能够在接下来的情节中学习在每个环境状态下，选择和执行环境中哪个动作更好，这会导致进一步的增强。平均而言，我们提出的算法在实现算法之前将处理器、内存和带宽方面的 HM 间负载平衡分别提高了 25%、34% 和 32%。我们的策略从三个场景的不同方面与 MOVMrB 策略进行了比较。最后，得出的结论是，我们提出的算法可以通过更少的运行时间和关闭更多的 HM 来更有效地进行负载平衡。