The rapid growth of cloud computing in the last decade has led to an increasing concern about the energy requirement of cloud data centers. Dynamic virtual machine (VM) consolidation is an effective way to tackle this issue, where VMs are executed on as few physical machines (PMs) as possible. Meanwhile, VM placement must be performed strategically, by considering different factors of the available resources to optimal exploitation of them. Moreover, a major challenge is the system’s reliability degradation because of the high frequency of consolidation and placing VMs on unreliable PMs. In this paper, we address the problem by introducing a discrete-time Markov chain (DTMC) model to predict future resource usage. Using the DTMC model along with the reliability model of PMs leads to more accurate PMs categorization based on their status. Then, a multi-objective VM placement approach is proposed to achieve the optimal VMs to PMs mapping using the $\epsilon$-dominance-based multi-objective artificial bee colony ($\epsilon$-MOABC) algorithm which can efficiently balance the overall energy consumption, resource wastage, and the system reliability to meet SLA and QoS requirements. We have validated the effectiveness of our proposed approach by conducting a performance evaluation study using the CloudSim toolkit. Competitive analysis of the experimental results demonstrates that the proposed approach significantly improves energy consumption while avoiding the inefficient VM migrations.

在过去的十年中，云计算的快速增长导致人们越来越关注云数据中心的能源需求。动态虚拟机（VM）整合是解决此问题的有效方法，其中VM在尽可能少的物理机（PM）上执行。同时，必须通过考虑可用资源的不同因素来优化利用虚拟机的位置，从而有策略地执行虚拟机的放置。此外，主要的挑战是系统的可靠性下降，这是因为合并的频率很高，并且将VM放置在不可靠的PM上。在本文中，我们通过引入离散时间马尔可夫链（DTMC）模型来预测未来的资源使用情况来解决该问题。结合使用DTMC模型和PM的可靠性模型，可以根据PM的状态进行更准确的分类。然后，$\epsilon$基于优势的多目标人工蜂群（$\epsilon$-MOABC）算法，可以有效地平衡总体能耗，资源浪费和系统可靠性，从而满足SLA和QoS要求。我们已经使用CloudSim工具包进行了性能评估研究，从而验证了我们提出的方法的有效性。对实验结果的竞争分析表明，所提出的方法可显着提高能耗，同时避免了无效的VM迁移。