Enhancing Cloud Service Performance Using Machine Learning-Based Anomaly Detection - Dhananjay Kumar,; Jeetendra Singh Yadav, (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
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.