AI-Based Workload Balancing for Distributed Systems

  • Authors

    • Liam Walker Technology Manager, HSBC, UK. Author
    • Grace Young Finance Director, Barclays, UK. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2022PI9M3A

    Published 02-04-2022

  • Distributed Systems, Workload Balancing, Artificial Intelligence, Machine Learning, Reinforcement Learning, Cloud Computing, Resource Management

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    L. Walker and G. Young, “AI-Based Workload Balancing for Distributed Systems”, ijmiet, vol. 5, no. 1, pp. 01–13, Feb. 2022, doi: 10.67228/30715628/IJMIET-2022PI9M3A.

  • Abstract

    The Artificial Intelligence (AI) has become a disruptive enabler in the distributed systems workload balancing addressing the long-term issue related to the distribution, the heterogeneity, the uncertainty, and the availability of the resource dynamically. The traditional methods of workload balancing, including fixed partitioning, round-robin, and load-sharing based on heuristics, are not usually applicable to the modern distributed systems based on the cloud computing architectures, edge/fog systems, Internet of Things (IoT) systems, and other systems with the intensive usage of data sizes. In this paper, workload balancing methods in distributed systems based on AI are undertaken systematically and in depth as experienced in their theoretical background, architectures, algorithms and performance implications. The paper expounds on machine learning, deep learning, reinforcement learning, and hybrid AI techniques adopted in the intelligent workload allocation, task migration, and adaptive resource management. An elaborate literature review is performed, including the traditional load balancing approaches and their development to the AI-based ones. The suggested approach describes an AI-based workload balancing model and model that includes the workload prediction, model of system state, smart decision-making, and continuous learning. Thousands of experiments and discussions prove that AI-based workload balancing is effective regarding a shorter response time, better resource consumption, a higher scale, and fault tolerance. Lastly, the paper identifies open research opportunities and future directions, including the topics of explainable AI, federated learning, and energy-conscious scheduling in the next-generation distributed systems.

  • References

    [1] T. L. Casavant and J. G. Kuhl, “A taxonomy of scheduling in general-purpose distributed computing systems,” IEEE Transactions on Software Engineering, vol. 14, no. 2, pp. 141–154, 1988.

    [2] E. Gelenbe and R. Iasnogorodski, “A queue with random service and arrivals,” Journal of Applied Probability, vol. 17, no. 1, pp. 211–220, 1980.

    [3] R. Buyya, C. S. Yeo, and S. Venugopal, “Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilities,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599–616, 2009.

    [4] M. Mitzenmacher, “The power of two choices in randomized load balancing,” IEEE Transactions on Parallel and Distributed Systems, vol. 12, no. 10, pp. 1094–1104, 2001.

    [5] H. Xu and B. Li, “Dynamic cloud pricing for revenue maximization,” IEEE Transactions on Cloud Computing, vol. 1, no. 2, pp. 158–171, 2013.

    [6] S. P. Ahuja, M. Mohanty, and N. Mohanty, “Load balancing in cloud computing: A survey,” International Journal of Computer Science and Information Technologies, vol. 5, no. 3, pp. 3175–3179, 2014.

    [7] J. Dean and L. A. Barroso, “The tail at scale,” Communications of the ACM, vol. 56, no. 2, pp. 74–80, 2013.

    [8] C. Delimitrou and C. Kozyrakis, “Paragon: QoS-aware scheduling for heterogeneous datacenters,” Proceedings of the 18th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp. 77–88, 2013.

    [9] Y. Mao, J. Zhang, and K. B. Letaief, “Dynamic computation offloading for mobile-edge computing with energy harvesting devices,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 12, pp. 3590–3605, 2016.

    [10] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

    [11] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.

    [12] R. Sutton and A. Barto, Reinforcement Learning: An Introduction, 2nd ed., MIT Press, 2018.

    [13] V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.

    [14] Z. Xu, X. Wang, and Y. Zhang, “A reinforcement learning approach for load balancing in cloud data centers,” IEEE International Conference on Cloud Computing, pp. 57–64, 2017.

    [15] Y. Zhang, M. Chen, S. Mao, L. Hu, and V. Leung, “CAP: Community activity prediction based on deep learning,” IEEE Network, vol. 30, no. 2, pp. 26–33, 2016.

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