Performance Analysis of Proxy-Based Object-Oriented Distributed Systems Using Game Theory

Document Type : Original Article

Authors

1 Department of Computer Engineering, Rasht branch, Islamic Azad University, Guilan, Iran

2 Department of Computer Engineering, Rasht Rasht Branch, Islamic Azad University Rasht, Iran

3 Young Researchers and Elite Club Rasht Branch, Islamic Azad University Rasht, Iran

Abstract

Recently, there has been a remarkable growth of research on the practical applications of game theory in networks, and in particular, the modeling of users’ behavior in distributed and decentralized systems. Reducing the runtime of operations in these types of systems will increase their performance. In order to achieve this goal, the system can be implemented using an object-oriented approach, through which the client machine treats the srver machine as an object, and the communication between them is done only through a proxy. In these types of systems, users have a set of possible choices, and may choose personal benefits over the interest of the whole system and other users. Since in a distributed system, all users want to control their resource of choice, the use of game theory can be a good tool to evaluate the behavior of selfish nodes. In this paper, game theory is used to investigate the behavior of nodes in an object-oriented distributed system, in which the communication between the client machine and the server machine is established through a proxy. To understand the behavior of nodes in a distributed system,one-time games and infinitely-repeated games are studied, and finally, the behavior of one node against an object-oriented distribution system is analyzed. According to the results of this study, nodes defect and will be uncooperative in one-time games. But when there is a strategy of an infinitely-repeated game, the cooperation between nodes will depend on the discount factor, or the probability of the next stage.

Keywords

References

Abraham, I., L. Alvisi and J. Y. Halpern (2011). "Distributed computing meets game theory: combining insights from two fields." Acm Sigact News 42(2): 69-76. DOI:10.1145/1998037.1998055
Abraham, I., D. Dolev and J. Y. Halpern (2019). "Distributed Protocols for Leader Election: A Game-Theoretic Perspective." ACM Transactions on Economics and Computation (TEAC) 7(1): 4. DOI:10.1007/s11227-021-03803-7
Asadi, M., A. Agah and C. Zimmerman (2017). "Applying Game Theory in Securing Wireless Sensor Networks by Minimizing Battery Usage." Game Theory: Breakthroughs in Research and Practice: Breakthroughs in Research and Practice: 337. DOI:10.1007/s11227-021-03803-7
Asadi, M., A. Agah and C. Zimmerman (2018). Applying Game Theory in Securing Wireless Sensor Networks by Minimizing Battery Usage. Game Theory: Breakthroughs in Research and Practice, IGI Global: 337-352. DOI: 10.4018/978-1-4666-4789-3.ch004
Colman, A. M. (2016). Game theory and experimental games: The study of strategic interaction, Elsevier. DOI:10.1016/j.automatica.2018.10.041
Deng, Z. and S. Liang (2019). "Distributed algorithms for aggregative games of multiple heterogeneous Euler–Lagrange systems." Automatica 99: 246-252. DOI:10.1016/j.automatica.2018.10.041
Ghosh, P., N. Roy, S. K. Das and K. Basu (2005). "A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework." Journal of Parallel and Distributed Computing 65(11): 1366-1383. DOI:10.1016/j.jpdc.2005.05.013
Gupta, R. and A. K. Somani (2005). Game theory as a tool to strategize as well as predict nodes' behavior in peer-to-peer networks. Parallel and Distributed Systems, 2005. Proceedings. 11th International Conference on, IEEE. DOI:10.1109/ICPADS.2005.157
Khan, S. U. and I. Ahmad (2006). Non-cooperative, semi-cooperative, and cooperative games-based grid resource allocation. Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International, IEEE. DOI:10.1109/IPDPS.2006.1639358
Kwok, Y.-K., S. Song and K. Hwang (2005). Selfish grid computing: game-theoretic modeling and NAS performance results. Cluster Computing and the Grid, 2005. CCGrid 2005. IEEE International Symposium on, IEEE. DOI:10.1109/IPDPS.2006.1639358
Looney, J. and T. Schlossnagle (2017). "Distributed Systems Reasoning." doi:10.1111/ecin.13178
Mailath, G. J. (2019). "Modeling Strategic Behavior: A Graduate Introduction to Game Theory and Mechanism Design." World Scientific Books. doi:10.1016/j.arcontrol.2022.02.002
Marden, J. R. and J. S. Shamma (2018). "Game-theoretic learning in distributed control." Handbook of Dynamic Game Theory: 511-546. DOI:10.1109/TVT.2019.2902318
Messous, M. A., S.-M. Senouci, H. Sedjelmaci and S. Cherkaoui (2019). "A Game Theory Based Efficient Computation Offloading in an UAV Network." IEEE Transactions on Vehicular Technology. DOI:10.1109/TVT.2019.2902318
Mishkovski, I., S. Filiposka, D. Trajanov, A. Grnarov and L. Kocarev "Using Game Theory to Analyze Distributed Computing Systems." DOI:10.1109/TVT.2019.2902318
Tanenbaum, A. S. and M. Van Steen (2007). Distributed systems: principles and paradigms, Prentice-Hall. DOI:10.1109/TVT.2019.2902318
Valone, T. J., Z. Barta, J. Börner, J.-C. Cardenas, L.-A. Giraldeau, H. Kokko, J. A. Oldekop, D. Pauly, D. Rustagi and W. J. Sutherland (2017). "Insights from Game Theory." Investors and Exploiters in Ecology and Economics: Principles and Applications: 97. DOI:10.1109/TVT.2019.2902318
Webster, T. J. (2018). Introduction to game theory in business and economics, Routledge. DOI:10.1109/TVT.2019.2902318
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