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

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@article{23d7032524ff47cfad12a8c9dc5340db,
title = "Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centers",
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.",
keywords = "Cloud computing, Measurement, Prediction algorithms, Reliability, Servers, Switches, Virtual machining, Virtual machine consolidation, cloud computing, data centers, multiple resource prediction, virtual machine migration",
author = "Nguyen, {T. H.} and Francesco, {M. Di} and A. Yla-Jaaski",
note = "K{\"a}sikirjoitus avataan, kun artikkeli julkaistu",
year = "2017",
doi = "10.1109/TSC.2017.2648791",
language = "English",
journal = "IEEE TRANSACTIONS ON SERVICES COMPUTING",
issn = "1939-1374",
publisher = "Institute of Electrical and Electronics Engineers",

}

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TY - JOUR

T1 - Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centers

AU - Nguyen, T. H.

AU - Francesco, M. Di

AU - Yla-Jaaski, A.

N1 - Käsikirjoitus avataan, kun artikkeli julkaistu

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Cloud computing

KW - Measurement

KW - Prediction algorithms

KW - Reliability

KW - Servers

KW - Switches

KW - Virtual machining

KW - Virtual machine consolidation

KW - cloud computing

KW - data centers

KW - multiple resource prediction

KW - virtual machine migration

U2 - 10.1109/TSC.2017.2648791

DO - 10.1109/TSC.2017.2648791

M3 - Article

JO - IEEE TRANSACTIONS ON SERVICES COMPUTING

JF - IEEE TRANSACTIONS ON SERVICES COMPUTING

SN - 1939-1374

ER -

ID: 16813857