In recent years, cloud computing has become more popular because of advancements in virtualization technology. By increasing the number of servers in cloud computing environment, cloud data centers have expanded and consumed much energy. Virtual machine consolidation is a solution for energy management in cloud environment. On the other hand, by increasing resource utilization in virtual machine consolidation, service level agreement assurance is difficult to obtain. Two main challenges in virtual machine consolidation are timely detection of overloaded servers and proper immigrant virtual machine selection from detected servers. In this paper, a new model is proposed based on MAPE‐k loop for autonomous virtual machine selection. The presented model uses a proposed ensemble prediction algorithm in the analysis phase. Also, in the planning phase, a new multi‐heuristics algorithm with flexible weights using learning automata is proposed. The effectiveness of the proposed model is evaluated by CloudSim simulator under real workload as compared with well‐known algorithms in this domain. The experimental results indicate that, the proposed approach has averagely improved the balance between service level agreement violations, energy and migration counts by 47.39% compared to other methods.

中文翻译：

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通过学习云环境中的自动机来自动优化虚拟机选择的自主模型

近年来，由于虚拟化技术的进步，云计算已变得越来越流行。通过增加云计算环境中的服务器数量，云数据中心已经扩展并消耗了很多能量。虚拟机整合是云环境中能源管理的解决方案。另一方面，通过提高虚拟机整合中的资源利用率，很难获得服务级别协议的保证。虚拟机整合中的两个主要挑战是及时检测过载的服务器以及从检测到的服务器中正确选择移民虚拟机。本文提出了一种基于MAPE-k循环的新模型，用于自主虚拟机选择。提出的模型在分析阶段使用了建议的整体预测算法。另外，在计划阶段，提出了一种基于学习自动机的具有加权权重的新型多启发式算法。与该领域的知名算法相比，CloudSim模拟器在实际工作负载下评估了所提出模型的有效性。实验结果表明，与其他方法相比，该方法平均将服务水平协议违规，能源和迁移计数之间的平衡提高了47.39％。