THE APPLICATION OF EVOLUTIONARY GAME IN RESOURCE ALLOCATION
DOI:
https://doi.org/10.53555/eijse.v6i2.38Keywords:
Resource allocation, Nash equilibrium, evolutionary gameAbstract
Resource allocation could not arrive at Nash equilibrium directly as that under completed rationality, due to bounded rationality of users. A resource allocation strategy based on evolutionary game is proposed to investigate the evolutionary process of user colony from the dynamic viewpoint. Using the method of replicated dynamics, an evolutionary stable strategy is produced to allocate resource. In particular, the evolutionary stable point, evaluation function characteristics, and replicated dynamic diagrams are discussed under different conditions. The results show that using evolutionary game approach, users could study and adjust strategy constantly through repeated games to achieve evolutionary stable equilibrium, which leads to an optimal allocation of resource.
References
. Sugang Ma. “A Review on Cloud Computing Development”, Journal of Networks. 2012, 7(2): 305-311.
. Radhika Batra, Naveen Sharma. “Cloud Testing: A Review Article”, International Journal of Computer Science and Mobile Computting. 2014, 3(6): 314-319.
. Roy S, “Game Theory: An Overview,” The ICFAI Journal of Managerial Economics. 2005, 3(4): 46-53.
. Kwok Y K, ShanShan Song, Kai Hwang, “Selfish Grid Computing: Game-Theoretic Modeling and NAS Performance Results”. Proceedings of the IEEE International Symposium on Cluster Computing and the Grid [C], 2005. pp. 349-356.
. Ghosh P, “A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework,” Journal of Parallel and Distributed Computing, 2005, 65(11): 1366-1383.
. Abramson D, Buyya R, Giddy J, “A Computational Economy for Grid Computing and its Implementation in the Nimrod-G Resource Broker,” Future Generation Computer Systems, 2002, 18(8):1061-1074.
. Bredin J, Kotz D, Rus D, et al. “Computational Markets to Regulate Mobile-Agent Systems,” Autonomous Agents and Multi-Agent Systems, 2003, 6(3): 235-263.
. Maheswaran R T, Başar T, “Nash Equilibrium and Decentralized Negotiation in Auctioning Divisible Resources,” Group Decision and Negotiation, 2003, 12(5): 361-395.
. Buyya R, Abramson D, Giddy J, “A Case for Economy Grid Architecture for Service-Oriented Grid Computing,” Proceedings of the 10th IEEE International Heterogeneous Computing Workshop, 2001. pp. 776-790.
. Fatima S S, Wooldridge M, Jennings N R, “A comparative study of game theoretic and evolutionary models of bargaining for software agents,” Artificial Intelligence Review, 2005, 23(2):185-203.
. Antal T, Scheuring I, “Fixation of strategies for an evolutionary game in finite populations,” Bulletin of Mathematical Biology, 2006, 68(8):1923-1944.
. Taylor P D, “Evolutionarily Stable Strategies with Two Types of Player,” Journal of Applied Probability, 1979, 16(1): 76-83.
. Buyya R, Murshed M, “GridSim: A Toolkit for Modeling and Simulation of Grid Resource Management and Scheduling,” Journal of Concurrency and Computation: Practice and Experience, 2002, 14(13-15): 1175– 1220.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 EPH - International Journal of Science And Engineering (ISSN: 2454 - 2016)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
