Energy-aware virtual machine consolidation for cloud data centers

Dabiah Ahmed M Al Alboaneen; Bernardi Pranggono; Huaglory Tianfield

doi:10.1109/UCC.2014.166

Energy-aware virtual machine consolidation for cloud data centers

Dabiah Ahmed M Al Alboaneen, Bernardi Pranggono, Huaglory Tianfield

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

One of the issues in virtual machine consolidation (VMC) in cloud data centers is categorizing different workloads to classify the state of physical servers. In this paper, we propose a new scheme of host's load categorization in energy-performance VMC framework to reduce energy consumption while meeting the quality of service (QoS) requirement. Specifically the under loaded hosts are classified into three further states, i.e., Under loaded, normal and critical by applying the under load detection algorithm. We also design overload detection and virtual machine (VM) selection policies. The simulation results show that the proposed policies outperform the existing policies in Cloud Sim in terms of both energy and service level agreements violation (SLAV) reduction.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing
Publisher	IEEE
Pages	1010-1015
Number of pages	6
ISBN (Print)	9781479978816
DOIs	https://doi.org/10.1109/UCC.2014.166
Publication status	Published - Dec 2014

Keywords

virtual machine consolidation (VMC)
energy-aware
energy-efficient
cloud data centres

Documents and Links

10.1109/UCC.2014.166

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Alboaneen, D. A. M. A., Pranggono, B., & Tianfield, H. (2014). Energy-aware virtual machine consolidation for cloud data centers. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (pp. 1010-1015). IEEE. https://doi.org/10.1109/UCC.2014.166

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title = "Energy-aware virtual machine consolidation for cloud data centers",

abstract = "One of the issues in virtual machine consolidation (VMC) in cloud data centers is categorizing different workloads to classify the state of physical servers. In this paper, we propose a new scheme of host's load categorization in energy-performance VMC framework to reduce energy consumption while meeting the quality of service (QoS) requirement. Specifically the under loaded hosts are classified into three further states, i.e., Under loaded, normal and critical by applying the under load detection algorithm. We also design overload detection and virtual machine (VM) selection policies. The simulation results show that the proposed policies outperform the existing policies in Cloud Sim in terms of both energy and service level agreements violation (SLAV) reduction.",

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AU - Pranggono, Bernardi

AU - Tianfield, Huaglory

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N2 - One of the issues in virtual machine consolidation (VMC) in cloud data centers is categorizing different workloads to classify the state of physical servers. In this paper, we propose a new scheme of host's load categorization in energy-performance VMC framework to reduce energy consumption while meeting the quality of service (QoS) requirement. Specifically the under loaded hosts are classified into three further states, i.e., Under loaded, normal and critical by applying the under load detection algorithm. We also design overload detection and virtual machine (VM) selection policies. The simulation results show that the proposed policies outperform the existing policies in Cloud Sim in terms of both energy and service level agreements violation (SLAV) reduction.

AB - One of the issues in virtual machine consolidation (VMC) in cloud data centers is categorizing different workloads to classify the state of physical servers. In this paper, we propose a new scheme of host's load categorization in energy-performance VMC framework to reduce energy consumption while meeting the quality of service (QoS) requirement. Specifically the under loaded hosts are classified into three further states, i.e., Under loaded, normal and critical by applying the under load detection algorithm. We also design overload detection and virtual machine (VM) selection policies. The simulation results show that the proposed policies outperform the existing policies in Cloud Sim in terms of both energy and service level agreements violation (SLAV) reduction.

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KW - energy-efficient

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