Energy Saving Task Consolidation Technique in Cloud Centers with Resource Utilization Threshold

被引:3
作者
Gourisaria, Mahendra Kumar [1 ]
Patra, S. S. [2 ]
Khilar, P. M. [3 ]
机构
[1] KIIT Univ, Sch Comp Engn, Bhubaneswar 751024, Odisha, India
[2] KIIT Univ, Sch Comp Applicat, Bhubaneswar 751024, Odisha, India
[3] Natl Inst Technol Rourkela, Dept Comp Sci & Engn, Rourkela 769008, Odisha, India
来源
PROGRESS IN ADVANCED COMPUTING AND INTELLIGENT ENGINEERING, PROCEEDINGS OF ICACIE 2016, VOLUME 1 | 2018年 / 563卷
关键词
Cloud computing; Virtual cluster; MaxUtil; ECTC; Energy efficient;
D O I
10.1007/978-981-10-6872-0_63
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The data centers are the world's biggest consumers of electricity. The consumption of energy in the cloud is proportional to the CPU utilization of the virtual machines (VMs). As the size of the cloud infrastructure increases the complexity of the resource allocation problem increases and becomes very difficult to solve it efficiently. This is an NP-Hard problem. There are several heuristics that may be used to solve the problem. Through task consolidation, we can get many benefits such as maximizing cloud computing resource, utilization of resources in a better way, efficient use of power, customization of IT services, Quality of Service, and other reliable services, etc. We find from the literature review that there is a high level of coupling between energy consumption and resource utilization. This paper presents the resource allocation problem in cloud computing with the objective to minimize energy consumed in computation. The simulation results show that a 70% principle of CPU utilization is the most energy efficient threshold for task consolidation in a virtual cluster. It has been verified with MaxUtil and ECTC (Energy Conscious Task Consolidation) algorithms.
引用
收藏
页码:655 / 666
页数:12
相关论文
共 16 条
[1]  
Ali S., 2000, Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556), P185, DOI 10.1109/HCW.2000.843743
[2]  
[Anonymous], 2013, THESIS
[3]  
Bojanova I, 2011, P INT C ADV INF NETW, P45
[4]  
Ching-Hsien Hsu, 2011, Proceedings of the 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science (CloudCom 2011), P115, DOI 10.1109/CloudCom.2011.25
[5]  
Fan XB, 2007, CONF PROC INT SYMP C, P13, DOI 10.1145/1273440.1250665
[6]  
Hsu C, 2011, 3 IEEE INT C CLOUD C
[7]   Optimizing energy consumption with task consolidation in clouds [J].
Hsu, Ching-Hsien ;
Slagter, Kenn D. ;
Chen, Shih-Chang ;
Chung, Yeh-Ching .
INFORMATION SCIENCES, 2014, 258 :452-462
[8]  
Kim KH, 2007, CCGRID 2007: SEVENTH IEEE INTERNATIONAL SYMPOSIUM ON CLUSTER COMPUTING AND THE GRID, P541
[9]   Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems [J].
Kim, Nakku ;
Cho, Jungwook ;
Seo, Euiseong .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE, 2014, 32 :128-137
[10]  
Koomey Jonathan., 2011, Growth in data center electricity use 2005 to 2010. A report by Analytical Press, V9