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

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Abstract

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.

Details

Original languageEnglish
Number of pages14
JournalIEEE TRANSACTIONS ON SERVICES COMPUTING
Publication statusE-pub ahead of print - 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • Cloud computing, Measurement, Prediction algorithms, Reliability, Servers, Switches, Virtual machining, Virtual machine consolidation, cloud computing, data centers, multiple resource prediction, virtual machine migration

ID: 16813857