Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.knosys.2017.09.040 | Knowledge-Based Systems |
Keywords | Field | DocType |
Cloud computing,Bacteria foraging optimization algorithm,Load balance | Bacteria foraging,Computer science,Load balancing (computing),Evolutionary theory,Algorithm,Online and offline,Perfect information,Computation,Cloud computing,Distributed computing | Journal |
Volume | ISSN | Citations |
138 | 0950-7051 | 2 |
PageRank | References | Authors |
0.38 | 17 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tang Lin-Lin | 1 | 31 | 12.25 |
| Li Zuohua | 2 | 3 | 3.11 |
| Ren Pingfei | 3 | 2 | 0.38 |
| Pan Jeng-Shyang | 4 | 2466 | 269.74 |
| Lu Zhe-Ming | 5 | 635 | 72.85 |
| Jing-yong Su | 6 | 156 | 10.93 |
| Meng ZhenYu | 7 | 50 | 6.45 |