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



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.



Total Pages: 7
Pages: 547-553


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Volume: 23
Issue: 4
Year: 2017

Cite this document


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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
PREVIOUS DOI PREFIX (with T&F): 10.1080/10798587
InCites Journal IMPACT FACTOR (JIF) Data

2018  0.790
2017  0.652
2016  0.644

Scimago Journal and Country Rank (SJR) Data

2018  0.993
2017  0.655
2016  0.660
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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