Enhancing Cloud Service Performance Using Machine Learning-Based Anomaly Detection
Abstract
Cloud computing has become an essential backbone for modern digital services, offering scalable, on-demand resources to meet dynamic user needs. However, maintaining optimal performance and reliability in cloud environments is challenging due to the complexity and unpredictability of system behavior. This paper proposes a machine learning-based anomaly detection framework to enhance cloud service performance. The proposed approach utilizes advanced algorithms such as Support Vector Machines (SVM), Random Forest, and Long Short-Term Memory (LSTM) networks to identify abnormal patterns in real-time system metrics, including CPU utilization, memory usage, network latency, and disk I/O. By accurately detecting anomalies and initiating proactive corrective actions, the system minimizes downtime, prevents service degradation, and optimizes resource utilization. Experimental results on benchmark cloud datasets demonstrate the effectiveness of the proposed model in achieving high detection accuracy with low false alarm rates. This research highlights the potential of intelligent anomaly detection systems in ensuring robust, efficient, and resilient cloud service delivery. Keywords: 4 or 5 key words or phrases in alphabetical order, separated by comma.
Downloads
References
Armbrust, M., Fox, A., Griffith, R., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
[Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1–58.
[Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.
D. F. Pedroso, L. Almeida, L. E. G. Pulcinelli, W. A. A. Aisawa, I. Dutra and S. M. Bruschi, "Anomaly Detection and Root Cause Analysis in Cloud-Native Environments Using Large Language Models and Bayesian Networks," in IEEE Access, vol. 13, pp. 77550-77564, 2025, doi: 10.1109/ACCESS.2025.3565220.
H. Le, T. Hoang, M. Gabriel, G. Neumann and N. A. Vien, "Enhancing Exploration With Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation," in IEEE Robotics and Automation Letters, vol. 10, no. 6, pp. 6143-6150, June 2025, doi: 10.1109/LRA.2025.3564780.
H. Goumidi and S. Pierre, "Real-Time Anomaly Detection in IoMT Networks Using Stacking Model and a Healthcare- Specific Dataset," in IEEE Access, vol. 13, pp. 70352-70365, 2025, doi: 10.1109/ACCESS.2025.3563158.
M. Al-Sharafi et al., "Ensuring Zero Trust Security in Consumer Internet of Things Using Federated Learning-Based Attack Detection Model," in IEEE Access, vol. 13, pp. 54423-54438, 2025, doi: 10.1109/ACCESS.2025.3551212.
P. Renukadevi, S. Amaran, A. Vikram, T. Prabhakara Rao and M. K. Ishak, "Enhancing Cybersecurity Through Fusion of Optimization With Deep Wavelet Neural Networks on Denial of Wallet Attack Detection in Serverless Computing," in IEEE Access, vol. 13, pp. 47111-47122, 2025, doi: 10.1109/ACCESS.2025.3550735.
J. Liu et al., "SMore: Enhancing GPU Utilization in Deep Learning Clusters by Serverless-Based Co-Location Scheduling," in IEEE Transactions on Parallel and Distributed Systems, vol. 36, no. 5, pp. 903-917, May 2025, doi: 10.1109/TPDS.2025.3548320.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
