Citation :

Priya Nair, 2026. "Explainable Machine Learning for Identifying Resource Misuse in Shared Cloud Infrastructure" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 4, Issue 3: 18-33.

Abstract :

The introduction states that the use of shared cloud infrastructures is now fundamental to contemporary computing because it provides organizations with the ability to scale up their computing power, lower operational costs, and run many different types of applications. On the other hand, the fact that there are multiple users utilizing the same computing resources creates issues such as misuse of resources, poor allocation of resources, illegal usage of resources, and unusual behavior by the workloads. Many traditional monitoring systems use set limits and rules to determine what is normal or abnormal; however, these systems can fail to detect complex misuse patterns or provide an explanation as to why an anomaly was found. This paper is about the use of explainable machine learning techniques to find resource misuse in a shared cloud environment. A new conceptual model has been created that uses cloud resource monitoring, machine learning-based anomaly detection, and explainable machine learning techniques to enhance the accuracy, transparency, and interpretability of finding resource misuse. Some examples of relevant resource metrics would be CPU usage, memory usage, network traffic, storage operations, workload duration, and tenant access patterns. Machine learning algorithms can determine when a user is consuming too much of a resource, while explainable machine learning techniques like SHAP and LIME can help cloud administrators understand why the algorithm predicted something. Ultimately, this framework will allow administrators to make quicker decisions regarding resource misuse, investigate misuse more effectively, and manage resources responsibly. With the combination of predictive intelligence and explainable machine learning techniques, the conceptual model can strengthen cloud governance, maximize resource utilization, and eliminate unnecessary operational expenditures. In addition to discussing potential implementation issues such as data privacy, reliability of models, computational overhead, and scalability, the study identifies several areas for further research in trustworthy cloud security and resource optimization.

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Keywords :

Explainable Machine Learning, Cloud Computing, Resource Misuse, Shared Infrastructure, Anomaly Detection, SHAP, LIME, Resource Monitoring, Multi-Tenant Security, Cloud Resource Optimization