DocumentCode :
252068
Title :
Energy-Aware Virtual Machine Consolidation for Cloud Data Centers
Author :
Alboaneen, Dabiah Ahmed ; Pranggono, Bernardi ; Tianfield, Huaglory
Author_Institution :
Sch. of Eng. & Built Environ., Glasgow Caledonian Univ., Glasgow, UK
fYear :
2014
fDate :
8-11 Dec. 2014
Firstpage :
1010
Lastpage :
1015
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.
Keywords :
cloud computing; computer centres; energy conservation; power aware computing; virtual machines; QoS requirement; SLAV reduction; VM selection policy; VMC; cloud data centers; energy consumption reduction; energy-aware virtual machine consolidation; energy-performance VMC framework; load detection algorithm; overload detection policy; quality of service; service level agreements violation reduction; workload categorization; Bandwidth; Degradation; Energy consumption; Heuristic algorithms; Measurement; Quality of service; Virtual machining; cloud data center; energy-aware; energy-efficient; virtual machine consolidation (VMC);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Utility and Cloud Computing (UCC), 2014 IEEE/ACM 7th International Conference on
Conference_Location :
London
Type :
conf
DOI :
10.1109/UCC.2014.166
Filename :
7027633
Link To Document :
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