OPTIMIZING RESOURCE ALLOCATION IN DISTRIBUTED MACHINE LEARNING SYSTEMS: A HYBRID APPROACH COMBINING REINFORCEMENT LEARNING AND GAME THEORY

Authors

  • Butwall M Swami Keshvanand Institute of Technology, Management & Gramothan
  • Rajaan R Swami Keshvanand Institute of Technology, Management & Gramothan Jaipur

Keywords:

Reinforcement Learning (RL), Game Theory (GT), Resource Allocation, Distributed Machine Learning Systems, Hybrid Optimization Framework

Abstract

As machine learning models grow in complexity and scale, efficient resource allocation in distributed systems becomes a critical challenge. This paper introduces a novel hybrid approach combining Reinforcement Learning (RL) and Game Theory (GT) to optimize resource allocation in distributed machine learning systems. We propose a framework that leverages RL for dynamic resource management and GT for strategic decision-making among distributed nodes. The efficacy of our approach is evaluated through simulations and real-world benchmarks, demonstrating significant improvements in system performance and resource utilization.

References

Sutton, R.S., & Barto, A.G. (2018). Reinforcement Learning: An Introduction. MIT Press.

Nash, J.F. (1950). "Equilibrium Points in N-Person Games". Proceedings of the National Academy of Sciences, 36(1), 48-49.

Zhang, J., & Zhang, Y. (2022). "Dynamic Resource Allocation in Cloud Computing: A Survey". IEEE Transactions on Cloud Computing, 10(1), 123-145.

Sutton, R.S., & Barto, A.G. (2018). Reinforcement Learning: An Introduction. MIT Press.

Nash, J.F. (1950). "Equilibrium Points in N-Person Games". Proceedings of the National Academy of Sciences, 36(1), 48-49.

Zhang, J., & Zhang, Y. (2022). "Dynamic Resource Allocation in Cloud Computing: A Survey". IEEE Transactions on Cloud Computing, 10(1), 123-145.

Li, L., & Zhang, K. (2021). "Resource Allocation for Cloud Computing Using Reinforcement Learning". Journal of Cloud Computing: Advances, Systems and Applications, 10(1), 45-60.

Koutsoumanis, K., & Zervakis, M. (2022). "Game-Theoretic Approaches to Network Resource Allocation: A Survey". Computer Networks, 198, 108338.

Wu, Y., & Zhao, H. (2023). "An Efficient Hybrid Approach for Resource Management in Distributed Systems". ACM Transactions on Computational Logic, 24(2), 1-22.

Zhao, Y., & Liu, Q. (2021). "Deep Reinforcement Learning for Dynamic Resource Management in Edge Computing". IEEE Transactions on Network and Service Management, 18(3), 2345-2358.

Moreno, P., & Becerra, J. (2023). "Strategic Resource Allocation in Multi-Agent Systems Using Game Theory and Machine Learning". Artificial Intelligence Review, 56(1), 159-180.

Huang, X., & Wang, Y. (2022). "A Comparative Study of Resource Allocation Algorithms for Distributed Machine Learning Systems". Journal of Parallel and Distributed Computing, 157, 120-137.

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Published

2023-04-03

How to Cite

Butwall, M., & Rajaan, R. (2023). OPTIMIZING RESOURCE ALLOCATION IN DISTRIBUTED MACHINE LEARNING SYSTEMS: A HYBRID APPROACH COMBINING REINFORCEMENT LEARNING AND GAME THEORY. COMPUSOFT: An International Journal of Advanced Computer Technology, 12(00), 4004–4006. Retrieved from https://ijact.in/index.php/j/article/view/628

Issue

Section

Original Research Article

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