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Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centers

机译：节能型云数据中心的虚拟机整合和多种使用预测

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摘要

Virtual machine consolidation aims at reducing the number of active physical servers in a data center so as to decrease the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. Besides, virtual machine consolidation should take into account multiple resource types at the same time, since CPU is not the only critical resource in cloud data centers. In fact, also memory and network bandwidth can become a bottleneck, possibly causing violations in the service level agreement. This article presents a virtual machine consolidation algorithm with multiple usage prediction (VMCUP-M) to improve the energy efficiency of cloud data centers. In this context, multiple usage refers to both resource types and the horizon employed to predict future utilization. Our algorithm is executed during the virtual machine consolidation process to estimate the long-term utilization of multiple resource types based on the local history of the considered servers. The joint use of current and predicted resource utilization allows for a reliable characterization of overloaded and underloaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our solution through simulations on both synthetic and real-world workloads. The obtained results show that consolidation with multiple usage prediction reduces the number of migrations and the power consumption of the servers while complying with the service level agreement.
机译：虚拟机整合旨在减少数据中心中活动的物理服务器的数量，以减少总功耗。在这种情况下，大多数现有解决方案都依赖于积极的虚拟机迁移，从而导致不必要的开销和能源浪费。此外，虚拟机整合应同时考虑多种资源类型，因为CPU并不是云数据中心中唯一的关键资源。实际上，内存和网络带宽也可能成为瓶颈，可能导致违反服务级别协议。本文提出了一种具有多次使用预测的虚拟机整合算法（VMCUP-M），以提高云数据中心的能源效率。在这种情况下，多次使用是指资源类型和用于预测未来利用率的范围。我们的算法在虚拟机整合过程中执行，以根据考虑的服务器的本地历史记录估计多种资源类型的长期利用率。结合使用当前和预期的资源利用率，可以可靠地表征过载和负负载的服务器，从而减少合并后的负载和功耗。我们通过模拟综合和实际工作负载来评估我们的解决方案。获得的结果表明，使用多种使用情况预测进行合并可以减少迁移次数和服务器的功耗，同时又要符合服务水平协议。

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