Game-based scheduling algorithm to achieve optimize profit in mapreduce environment

Authors:
Cong Wan;Cuirong Wang;Ying Yuan;Haiming Wang
Affiliations:
College of Information Science and Engineering, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Year:
2013

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Abstract

MapReduce is a programming model and an associated implementation for processing and generating large data sets. Providing MapReduce as a service is the development future trend. By leveraging the game theory, this paper proposes a scheduling algorithm to deal with the competition for resources between multiple jobs in MapReduce. Firstly, we present a model that could estimate job executing time, and then a utility function of job and an optimization objective are brought forward; thirdly, we present a game model to solve the optimization problem. The proof and the solution are also present. Finally, we implement the algorithm and experiment it in a hadoop cluster. The result shows the present algorithm could schedule jobs rational.