A Hybrid Machine Learning and Control-Theoretic Framework for Stability-Assured Resource Management in Large-Scale Cloud Computing Environments
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I4P122Keywords:
Cloud Computing, Autoscaling, Resource Management, Machine Learning, Control Theory, Stability Assurance, Model Predictive Control, Kubernetes, Workload Forecasting, Service-Level ObjectivesAbstract
Large-scale cloud computing environments must continuously allocate, scale, and reconfigure resources under uncertain demand, multi-tenant interference, heterogeneous infrastructure, and stringent service-level objectives. Conventional threshold-based autoscaling remains widely used because of its operational simplicity, yet it often reacts after performance degradation has already occurred. Purely machine-learning-driven methods can improve prediction and adaptation, but they may produce unsafe actions when exposed to distribution shifts, delayed actuation, noisy telemetry, or unobserved dependencies. This paper proposes a hybrid machine learning and control-theoretic framework for stability-assured resource management in large-scale cloud computing environm ents. The framework integrates workload forecasting, online quality-of-service modeling, constrained optimization, feedback control, Lyapunov-style stability reasoning, and policy-governed decision intelligence. The proposed design separates predictive intelligence from safety-critical actuation: machine learning estimates near-future demand, performance sensitivity, and workload classes, while a constrained model-predictive controller and supervisory stability guard transform those estimates into resource actions that respect service-level, cost, and stability constraints. The framework is formulated for containerized and virtualized cloud platforms, including horizontal scaling, vertical resource adjustment, admission control, and workload placement. It defines a conceptual architecture, analytical stability conditions, evaluation metrics, and deployment implications for cloud operators. The analytical discussion shows that a hybrid design can reduce elastic lag, control oscillatory scaling behavior, preserve bounded latency error, and support auditable resource governance more effectively than purely reactive autoscaling or unconstrained learning policies. The paper contributes a structured research model for stability-aware cloud resource management and identifies future directions in safe reinforcement learning, distributed control, explainable autoscaling, and production-grade validation.
References
[1] J. L. Hellerstein, Y. Diao, S. Parekh, and D. M. Tilbury, Feedback Control of Computing Systems. Hoboken, NJ, USA: Wiley-IEEE Press, 2004. doi: 10.1002/047166880X.
[2] Gunda, S. K., Yettapu, S. D. R., Bodakunti, S., & Bikki, S. B. (2023). Decision Intelligence Methodology for AI-Driven Agile Software Lifecycle Governance and Architecture-Centered Project Management. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 102-108. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P112
[3] T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano, “A review of auto-scaling techniques for elastic applications in cloud environments,” Journal of Grid Computing, vol. 12, no. 4, pp. 559–592, Dec. 2014. doi: 10.1007/s10723-014-9314-7.
[4] K. J. Åström and R. M. Murray, Feedback Systems: An Introduction for Scientists and Engineers. Princeton, NJ, USA: Princeton University Press, 2008.
[5] Gunda, S. K. G. (2023). The Future of Software Development and the Expanding Role of ML Models. International Journal of Emerging Research in Engineering and Technology, 4(2), 126-129. https://doi.org/10.63282/3050-922X.IJERET-V4I2P113
[6] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “A view of cloud computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, Apr. 2010. doi: 10.1145/1721654.1721672.
[7] H. Mao, M. Alizadeh, I. Menache, and S. Kandula, “Resource management with deep reinforcement learning,” in Proceedings of the 15th ACM Workshop on Hot Topics in Networks (HotNets ’16), Atlanta, GA, USA, Nov. 2016, pp. 50–56. doi: 10.1145/3005745.3005750.
[8] A. Gandhi, M. Harchol-Balter, R. Raghunathan, and M. A. Kozuch, “AutoScale: Dynamic, robust capacity management for multi-tier data centers,” ACM Transactions on Computer Systems, vol. 30, no. 4, Article 14, Nov. 2012. doi: 10.1145/2382553.2382556.
[9] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599–616, Jun. 2009. doi: 10.1016/j.future.2008.12.001.
[10] T.-T. Nguyen, Y.-J. Yeom, T. Kim, D.-H. Park, and S. Kim, “Horizontal pod autoscaling in Kubernetes for elastic container orchestration,” Sensors, vol. 20, no. 16, Article 4621, Aug. 2020. doi: 10.3390/s20164621.
[11] D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang, “Power and performance management of virtualized computing environments via lookahead control,” Cluster Computing, vol. 12, no. 1, pp. 1–15, Mar. 2009. doi: 10.1007/s10586-008-0070-y.
[12] A. Verma, L. Pedrosa, M. Korupolu, D. Oppenheimer, E. Tune, and J. Wilkes, “Large-scale cluster management at Google with Borg,” in Proceedings of the Tenth European Conference on Computer Systems (EuroSys ’15), Bordeaux, France, Apr. 2015, Article 18, pp. 1–17. doi: 10.1145/2741948.2741964.
[13] J. O. Kephart and D. M. Chess, “The vision of autonomic computing,” Computer, vol. 36, no. 1, pp. 41–50, Jan. 2003. doi: 10.1109/MC.2003.1160055.
[14] Z. Zhou, C. Zhang, L. Ma, J. Gu, H. Qian, Q. Wen, L. Sun, P. Li, and Z. Tang, “AHPA: Adaptive horizontal pod autoscaling systems on Alibaba Cloud Container Service for Kubernetes,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 13, Washington, DC, USA, Feb. 2023, pp. 15621–15629. doi: 10.1609/aaai.v37i13.26852.
[15] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, vol. 41, no. 1, pp. 23–50, Jan. 2011. doi: 10.1002/spe.995.
[16] K. Rzadca, P. Findeisen, J. Świderski, P. Zych, P. Broniek, J. Kuśmierek, P. Nowak, B. Strack, P. Witusowski, S. Hand, and J. Wilkes, “Autopilot: Workload autoscaling at Google,” in Proceedings of the Fifteenth European Conference on Computer Systems (EuroSys ’20), Heraklion, Greece, Apr. 2020, Article 16, pp. 1–16. doi: 10.1145/3342195.3387524.
[17] S. Islam, J. Keung, K. Lee, and A. Liu, “Empirical prediction models for adaptive resource provisioning in the cloud,” Future Generation Computer Systems, vol. 28, no. 1, pp. 155–162, Jan. 2012. doi: 10.1016/j.future.2011.05.027.
[18] B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. Katz, S. Shenker, and I. Stoica, “Mesos: A platform for fine-grained resource sharing in the data center,” in Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’11), Boston, MA, USA, Mar. 2011, pp. 295–308. Available: https://www.usenix.org/conference/nsdi11/mesos-platform-fine-grained-resource-sharing-data-center.
[19] C. Delimitrou and C. Kozyrakis, “Quasar: Resource-efficient and QoS-aware cluster management,” in Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’14), Salt Lake City, UT, USA, Mar. 2014, pp. 127–144. doi: 10.1145/2541940.2541941.
[20] T. Chen and R. Bahsoon, “Self-adaptive and online QoS modeling for cloud-based software services,” IEEE Transactions on Software Engineering, vol. 43, no. 5, pp. 453–475, May 2017. doi: 10.1109/TSE.2016.2608826.
[21] L. A. Barroso, J. Clidaras, and U. Hölzle, the Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, 2nd ed. San Rafael, CA, USA: Morgan & Claypool, 2013. doi: 10.2200/S00516ED2V01Y201306CAC024.
[22] R. Nathuji and K. Schwan, “VirtualPower: Coordinated power management in virtualized enterprise systems,” in Proceedings of the 21st ACM SIGOPS Symposium on Operating Systems Principles (SOSP ’07), Stevenson, WA, USA, Oct. 2007, pp. 265–278. doi: 10.1145/1294261.1294287.
[23] P. Padala, K.-Y. Hou, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, and A. Merchant, “Automated control of multiple virtualized resources,” in Proceedings of the Fourth ACM European Conference on Computer Systems (EuroSys ’09), Nuremberg, Germany, Apr. 2009, pp. 13–26. doi: 10.1145/1519065.1519068.
[24] J. Dean and L. A. Barroso, “The tail at scale,” Communications of the ACM, vol. 56, no. 2, pp. 74–80, Feb. 2013. doi: 10.1145/2408776.2408794.
[25] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018. Available: http://incompleteideas.net/book/the-book-2nd.html.
