OPTIMIZING PROCESSOR WORKLOADS AND SYSTEM EFFICIENCY THROUGH GAME-THEORETIC MODELS IN DISTRIBUTED SYSTEMS
DOI:
https://doi.org/10.37943/19GBUY8720Keywords:
Game Theory, Nash Equilibria, processor optimization, distributed systems, strategic behavior, simulation algorithm, probabilistic approachAbstract
The primary goal of this research is to examine how different strategic behaviors adopted by processors affect the workload management and overall efficiency of the system. Specifically, the study focuses on the attainment of a pure strategy Nash Equilibrium and explores its implications on system performance. In this context, Nash Equilibrium is considered as a state where no player has anything to gain by changing only their own strategy unilaterally, suggesting a stable, yet not necessarily optimal, configuration under strategic interactions. The paper rigorously develops a formal mathematical model and employs extensive simulations to validate the theoretical findings, thus ensuring the reliability of the proposed model. Additionally, adaptive algorithms for dynamic task allocation are proposed, aimed at enhancing system flexibility and efficiency in real-time processing environments. Key results from this study highlight that while Nash Equilibrium fosters stability within the system, the adoption of optimal cooperative strategies significantly improves operational efficiency and minimizes transaction costs. These findings are illustrated through detailed 3D plots and tabulated results, which provide a detailed examination of how strategic decisions influence system performance under varying conditions, such as fluctuating system loads and migration costs. The analysis also examines the balance between individual processor job satisfaction and overall system performance, highlighting the effect of rigid task reallocation frameworks. Through this study, the paper not only improves our understanding of strategic interactions within computational systems but also provides key ideas that could guide the development of more efficient computational frameworks for various applications.
References
Chatterjee, S.R., Ghosh, S., & Chakraborty, M. (2021). Nash Bargaining in Resource Allocation for Cognitive Radio: A Review. Wireless Personal Communications, 118, 125 - 139. https://doi.org/10.1007/s11277-020-08005-7.
Abdalzaher, Mohamed & Muta, Osamu. (2019). Employing Game Theory and TDMA Protocol to Enhance Security and Manage Power Consumption in WSNs-based Cognitive Radio. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2940699.
Georgoulaki, E., Kollias, K., Tamir, T. (2021). Equilibrium Inefficiency and Computation in Cost-Sharing Games in Real-Time Scheduling Systems. Algorithms, 14:4(103). https://doi.org/10.3390/a14040103.
Wang, D., Li, K., Zhang, Q., Lu, X., & Luo, Y. (2021). A cooperative task allocation game for multi-target imaging in radar networks. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2021.3049899.
Zhang, H., Jiang, C., Beaulieu, N., Chu, X., Wang, X., & Quek, T. Q. S. (2015). Resource allocation for cognitive small cell networks: A cooperative bargaining game theoretic approach. IEEE Transactions on Wireless Communications, 14, 3481-3493. https://doi.org/10.1109/TWC.2015.2407355.
Agbaje, M. O., Ohwo, O. B., Ayanwola, T. G., & Ogunyolu, O. (2022). A survey of game-theoretic approach for resource management in cloud computing. Journal of Computer Networks and Communications, 2022, Article ID 9323818. https://doi.org/10.1155/2022/9323818.
Feldman, M., Snappir, Y., & Tamir, T. (2017). The efficiency of best-response dynamics. In Algorithmic Game Theory - 10th International Symposium, SAGT 2017, Proceedings (pp. 186-198). https://doi.org/10.1007/978-3-319-66700-3_15.
Kengne Tchendji, V., Yankam, Y. F., & Kombou Sihomnou, I. C. (2022). Game theory-based dynamic resource allocations scheme in virtual networks. Journal of Information and Telecommunication, 7(1), 1-28. https://doi.org/10.1080/24751839.2022.2117125.
Saadatfar, H., Gholampour Ahangar, H., & Hassannataj Joloudari, J. (2024). A new dynamic game-based pricing model for cloud environment. Future Internet, 16(2), 49. https://doi.org/10.3390/fi16020049.
Madani, K., Farhidi, F., & Gholizadeh, S. (2022). Bargaining power in cooperative resource allocations games. Algorithms, 15, 445. https://doi.org/10.3390/a15120445.
Chen, J., Deng, Q., & Yang, X. (2023). Non-cooperative game algorithms for computation offloading in mobile edge computing environments. Journal of Parallel and Distributed Computing, 172, 18-31. https://doi.org/10.1016/j.jpdc.2022.10.004.
Mulyadi, F., & Akkarajitsakul, K. (2019). Non-cooperative and cooperative game approaches for load balancing in distributed systems. In Proceedings of the 7th International Conference on Computer and Communications Management (ICCCM '19) (pp. 252–257). ACM, NY, USA. https://doi.org/10.1145/3348445.3348477.
Al-Gumaei, Y. A., Aslam, N., Al-Samman, A. M., Al-Hadhrami, T., Noordin, K., & Fazea, Y. (2019). Non-cooperative power control game in D2D underlying networks with variant system conditions. Electronics, 8, 1113. https://doi.org/10.3390/electronics8101113.
Sheng, M., Wang, H., Ma, M., Sun, Y. (2024). Risk assessment edge contract for efficient resource allocation. Mathematics, 12(7), 983. https://doi.org/10.3390/math12070983.
Li, H., Liang Ran, Lifeng Zheng, Zhe Li, Jinhui Hu. (2024). Convergence analysis of distributed generalized Nash equilibria seeking algorithm with asynchrony and delays. IEEE Transactions on Automatic Control. https://doi.org/10.1109/TAC.2024.3439652.
N. Okanami, R. Nakamura, and T. Nishide. (2020). Load balancing for sharded blockchains, Financial Cryptography and Data Security, pp.512-524, Springer International Publishing. https://doi.org/10.1007/978-3-030-54455-3_36.
Wang, Q., Zhou, Y., Ni, Y., Zhao, H., & Zhu, H. (2019). A review of game theoretical resource allocation methods in wireless communications. In 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, pp.881-887. https://doi.org/10.1109/ICCT46805.2019.8947117.
Moldabayev, D., Suchkov, M., Abdiakhmetova, Z., & Kartbayev, A. (2024). Developing game theory-based methods for modeling information confrontation in social networks. Scientific Journal of Astana IT University, 18, 17–29. https://doi.org/10.37943/18FONX7380.
Heinrich, T., Jang, Y., Mungo, L., et al. (2023). Best-response dynamics, playing sequences, and convergence to equilibrium in random games. International Journal of Game Theory, 52, 703-735. https://doi.org/10.1007/s00182-023-00837-4.
M. Suchkov, B. Bekturgan and A. Kartbayev. (2024). Gamification Effects on Employee Engagement and Business Process Risk Evaluation, 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST), Astana, Kazakhstan, pp. 594-599, doi: 10.1109/SIST61555.2024.10629629.
Breinbjerg, J., Platz, T., & Østerdal, L. (2023). Equilibrium arrivals to a last-come first-served preemptive-resume queue. Annals of Operations Research, 1-22. https://doi.org/10.1007/s10479-023-05348-9.
Haviv, Moshe & Ravner, Liron. (2020). A survey of queueing systems with strategic timing of arrivals. Queueing Syst 99, 163–198. https://doi.org/10.1007/s11134-021-09717-8.
Y. Zheng, C. Li and J. Feng. (2021) Modeling and Dynamics of Networked Evolutionary Game With Switched Time Delay, in IEEE Transactions on Control of Network Systems, vol. 8, no. 4, pp. 1778-1787. https://doi.org/10.1109/TCNS.2021.3084548.
Liron Ravner, Ran I. Snitkovsky (2023) Stochastic Approximation of Symmetric Nash Equilibria in Queueing Games. Operations Research. https://doi.org/10.1287/opre.2021.0306.
Roughgarden, T. (2005). Selfish Routing and The Price of Anarchy. MIT Press, ISBN: 978-0-262-18243-0.
Dimitriou, I. (2017). A queueing system for modeling cooperative wireless networks with coupled relay nodes and synchronized packet arrivals. Performance Evaluation, 114.
Chen, X., Epstein, L., & Kleiman, E. (2013). Maximizing the minimum load: The cost of selfishness. Theoretical Computer Science, 482, 9-19.
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