Online and offline based load balance algorithm in cloud computing

Linlin Tang, Zuohua Li, Pingfei Ren, Jengshyang Pan, Zheming Lu, Jingyong Su, Zhenyu Meng

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Cloud Computing (CC) makes it possible for a common user to get an access to large pools of data and computational resources through a variety of interfaces. Among the so many important problems in CC, load balancing technique has been paid more and more attention for its important role. Good load balance algorithms can make whole system run more efficient. A new load balancing method combined with the advantage of online and offline load balancing algorithms are proposed in this paper. Two-choice algorithm and its improvement are used in the online step. Bacteria Foraging Optimization (BFO) and its improvement motivated by Lamarck Evolutionary Theory are introduced in our offline step. Online load balancing uses imperfect information, aiming at finishing tasks as fast as possible; while the offline makes full use of all information to make a supplement. Experiments on the heterogeneous tasks and serving points for computation intensive loads have been used here and the good performance shows the efficiency of our proposed method.

Original languageEnglish
Pages (from-to)91-104
Number of pages14
JournalKnowledge-Based Systems
Volume138
DOIs
StatePublished - Dec 15 2017

Keywords

  • Bacteria foraging optimization algorithm
  • Cloud computing
  • Load balance

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