An Investigation into the Performance Optimization of Cloud Computing Systems Using Machine Learning Algorithms

References

[1] Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services in a cloud computing environment. Measurement, 119, 117-128.

[2] Osypanka, P., & Nawrocki, P. (2020). Resource usage cost optimization in cloud computing using machine learning. IEEE Transactions on Cloud Computing, 10(3), 2079-2089.

[3] Kamila, N. K., Frnda, J., Pani, S. K., Das, R., Islam, S. M., Bharti, P. K., & Muduli, K. (2022). Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach. Journal of King Saud University-Computer and Information Sciences, 34(10), 9991-10009.

[4] Sharkh, M. A., Xu, Y., & Leyder, E. (2020, August). CloudMach: cloud computing application performance improvement through machine learning. In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-6). IEEE.

[5] Kruekaew, B., & Kimpan, W. (2022). Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access, 10, 17803-17818.

[6] Butt, U. A., Mehmood, M., Shah, S. B. H., Amin, R., Shaukat, M. W., Raza, S. M., ... & Piran, M. J. (2020). A review of machine learning algorithms for cloud computing security. Electronics, 9(9), 1379.

[7] Shafiq, D. A., Jhanjhi, N. Z., Abdullah, A., & Alzain, M. A. (2021). A load balancing algorithm for the data centres to optimize cloud computing applications. Ieee Access, 9, 41731-41744.

[8] Nawrocki, P., & Smendowski, M. (2024). Optimization of the use of cloud computing resources using exploratory data analysis and machine learning. Journal of Artificial Intelligence and Soft Computing Research, 14(4), 287-308.

[9] Gupta, P., Goyal, M. K., Chakraborty, S., & Elngar, A. A. (Eds.). (2022). Machine learning and optimization models for optimization in the cloud. CRC Press.

[10] Hajimirzaei, B., & Navimipour, N. J. (2019). Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithms. Ict Express, 5(1), 56-59.

[11] Rjoub, G., Bentahar, J., Abdel Wahab, O., & Saleh Bataineh, A. (2021). Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems. Concurrency and Computation: Practice and Experience, 33(23), e5919.

[12] Talaat, F. M., Saraya, M. S., Saleh, A. I., Ali, H. A., & Ali, S. H. (2020). A load balancing and optimization strategy (LBOS) using reinforcement learning in a fog computing environment. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4951-4966.

[13] Sudhakar, R. V., Dastagiraiah, C., Pattem, S., & Bhukya, S. (2024). Multi-Objective Reinforcement Learning Based Algorithm for Dynamic Workflow Scheduling in Cloud Computing. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 12(3), 640-649.

[14] Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., & Deuse, J. (2020). Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing. Advanced engineering informatics, 45, 101101.

[15] Chen, X., Zhang, H., Wu, C., Mao, S., Ji, Y., & Bennis, M. (2018). Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning. IEEE Internet of Things Journal, 6(3), 4005-4018.

[16] Duc, T. L., Leiva, R. G., Casari, P., & Östberg, P. O. (2019). Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey. ACM Computing Surveys (CSUR), 52(5), 1-39.

[17] Murthy, P. (2020). Optimizing cloud resource allocation using advanced AI techniques: A comparative study of reinforcement learning and genetic algorithms in multi-cloud environments. World Journal of Advanced Research and Reviews, 2.

[18] Khayyat, M., Elgendy, I. A., Muthanna, A., Alshahrani, A. S., Alharbi, S., & Koucheryavy, A. (2020). Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks. IEEE Access, 8, 137052-137062.

[19] Lin, P., Ye, K., & Xu, C. Z. (2019). Dynamic network anomaly detection system by using deep learning techniques. In Cloud Computing–CLOUD 2019: 12th International Conference, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings 12 (pp. 161-176). Springer International Publishing.

[20] Abualigah, L., & Diabat, A. (2021). A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Computing, 24(1), 205-2

@article{1707965,
      author = {Sujon Sarkar},
      title = {An Investigation into the Performance Optimization of Cloud Computing Systems Using Machine Learning Algorithms},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {1357-1366},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707965.pdf},
      abstract = {Cloud computing is now the very backbone through which modern digital services operate, allowing on-demand access to computational resources and storage in an elastic manner. Nevertheless, the complexity, heterogeneity, and dynamic workload patterns of cloud environments have led to extensive challenges in performance management. Traditional static or rule-based optimization techniques that are often used are generally unable to accommodate real-time demand changes. Some performance issues resulting from this include inefficient resource utilization, service degradation, and increased operational costs. The objective of this study is the fusion of machine learning (ML) algorithms into improving the overall performance and efficiency of cloud computing systems. Some of the key performance indicators used in this study include CPU and Memory Utilization, latency, throughput, network traffic, and energy consumption. A comparative analysis has been carried out using the different ML paradigms, including supervised learning (for example, regression, decision trees), unsupervised learning (for example, clustering, anomaly detection), and reinforcement learning, to analyze their performance in different core optimization tasks, including dynamic workload balancing, auto-scaling prediction, fault tolerance, and proactive maintenance. A multi-layer a framework addressing real-time performance monitoring and adaptive decision-making via ML models trained on historical and streaming cloud telemetry data has been developed. A variety of case studies where data has been obtained from public cloud providers as a simulated environment show substantial improvements in both resource efficiency and system responsiveness. In addition, this research outlines trade-offs in model complexity and computational overhead and discusses what this could mean for the practical deployment of ML in production-grade cloud systems. Overall, this investigation confirms that intelligent, autonomous cloud operations can be achieved through ML-based optimization strategies far exceeding traditional heuristics. Hence, it opens a path toward creating self-optimizing cloud platforms satisfying the requirements of increasingly complex, real-time, and mission-critical applications.},
      keywords = {Cloud Computing, Performance Optimization, Machine Learning (ML), Resource Utilization, Proactive Maintenance, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Real-Time Monitoring, Elastic Infrastructure, Case Studies, Public Cloud Providers, Intelligent Cloud Operations, Autonomous Systems.},
      month = {April},
  }