Autosoft Journal

Online Manuscript Access


A Load Balancing Method For Massive Data Processing Under Cloud Computing Environment


Authors



Abstract

High processing efficiency and equalization are needed when a cloud computing system is used to deal with massive data. Better load balancing methods can further improve the data processing ability of the cloud computing system. In this paper, we first defined the data process efficiency (DPE) and relatively free rate (RFR). Then based on the DPE and RFR, we proposed a load balancing method for massive data (LBMM). And we further described the flow of the LBMM method. Finally, we compared the LBMM method and the consistent hashing method through experiments. The experimental results showed that the LBMM method had better data processing equalization and higher data processing efficiency.


Keywords


Pages

Total Pages: 7
Pages: 547-553

DOI
10.1080/10798587.2017.1316072


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways


Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Published

Volume: 23
Issue: 4
Year: 2017

Cite this document


References

Devine, Robert. "Design and Implementation of DDH: A Distributed Dynamic Hashing Algorithm." Lecture Notes in Computer Science (1993): 101-114. Crossref. Web. https://doi.org/10.1007/3-540-57301-1_7

Feng, Ye et al. "A Novel Cloud Load Balancing Mechanism in Premise of Ensuring QoS." Intelligent Automation & Soft Computing 19.2 (2013): 151-163. Crossref. Web. https://doi.org/10.1080/10798587.2013.786968

Ganesan P. Technical Report 2003-71

Ghosh, Bhaskar, and S. Muthukrishnan. "Dynamic Load Balancing by Random Matchings." Journal of Computer and System Sciences 53.3 (1996): 357-370. Crossref. Web. https://doi.org/10.1006/jcss.1996.0075

Iqbal, Saeed, and Graham F. Carey. "Performance Analysis of Dynamic Load Balancing Algorithms with Variable Number of Processors." Journal of Parallel and Distributed Computing 65.8 (2005): 934-948. Crossref. Web. https://doi.org/10.1016/j.jpdc.2005.04.003

Kaashoek, M. Frans, and Ion Stoica, eds. "Peer-to-Peer Systems II." Lecture Notes in Computer Science (2003): n. pag. Crossref. Web. https://doi.org/10.1007/b11823

Karger D. New algorithms for load balancing in peer-to-peer systems

Premarathne, Uthpala Subodhani et al. "Preference Based Load Balancing as an Outpatient Appointment Scheduling Aid." 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2013): n. pag. Crossref. Web. https://doi.org/10.1109/EMBC.2013.6609746

Shaji R.S. Journal of Emerging Technologies in Web Intelligence

Sharma S. World Academy of Science, Engineering and Technology

Xu, Cheng-Zhong, and Francis C. M. Lau. "Iterative Dynamic Load Balancing in Multicomputers." Journal of the Operational Research Society 45.7 (1994): 786-796. Crossref. Web. https://doi.org/10.1057/jors.1994.122

Zhao, Jia et al. "A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing." The Scientific World Journal 2013 (2013): 1-16. Crossref. Web. https://doi.org/10.1155/2013/492615

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/