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Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centers - 2017

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

Research Area:  Cloud Computing


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.


Author(s) Name:  Nguyen Trung Hieu; Mario Di Francesco and Antti Ylä-Jääski

Journal name:  IEEE Transactions on Services Computing

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TSC.2017.2648791

Volume Information:  Volume: 13, Issue: 1, Jan.-Feb. 1 2020, Page(s): 186 - 199