How to Avoid Herd Behavior: A Stochastic Multi-Choice Scheduling Algorithm and Parameters Analysis in Grid Scheduling

Haijun Yang (), Qinghua Zheng (), Minqiang Li () and Yuzhong Sun ()
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Haijun Yang: School of Economics and Management, Beihang University, Beijing 100191, China
Qinghua Zheng: School of Computer Science, Guangxi University of Science and Technology, Liuzhou, Guangxi 545006, China
Minqiang Li: College of Economics and Management, Tianjin University, Tianjin 300072, China
Yuzhong Sun: Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100180, China

International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 02, 287-315

Abstract: Large distributed systems, such as grid computing and cloud computing, promise to supply users with high performance. Consequently, scheduling is currently becoming a crucial problem. Herd behavior is a common phenomenon which causes severe performance decrease in the systems caused by bad scheduling behaviors. In this paper, based on the theoretical results of the homogeneous balls and bins model, it is proposed that a new and unique stochastic algorithm is used to avoid herd behavior. Experiments address that the multi-choice strategy can decrease herd behavior in large-scale sharing environment, at the same time providing increased scheduling performance and causing less scheduling burden than greedy algorithms. Distributed Hash Table (DHT) is used to organize grid computing resources. In the case of 1000 resources, the simulations show that for the heavy load (i.e., system utilization rate 0.5), the multi-choice algorithm reduces the number of incurred herds by a factor of 36, the average job waiting time by a factor of 8, and the average job turn-around time by 12% compared to the greedy algorithm. Moreover, in the cases of 2000 and 4000 nodes, two parameters (replica andd-group) are analyzed based on how they affect the performance of the algorithm. It is observed that there is an inflexion in the performance curve. Finally, a theoretic analysis of the algorithm performance is presented.

Keywords: Herd behavior; stochastic; scheduling algorithms; large-scale distributed systems (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0219622014500734

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