Explainable AI (XAI) in Predictive Cloud Optimization: Cost, Workload and Performance

The increasingly complex nature of cloud computing ecosystems used for highly scalable distributed applications requires advanced methodologies in optimizing resource distribution and workload prediction while achieving the optimal level of performance with reduced expenditure. Traditional approaches often become impractical in addressing the erratic nature of cloud workloads, resulting in inefficiency and potential overspending on costs for the enterprises. This research explores deep learning techniques like RNNs, LSTMs, GRUs and DNNs aimed at enhancing predictive capabilities for optimal cloud optimization regarding cost control, workload forecasting, and performance enhancement. However, the opaque nature of many artificial intelligence models is a significant barrier to their widespread adoption in critical cloud management tasks. The lack of transparency in AI-driven decisions can create concerns about trust, accountability, and compliance with regulatory standards. By using Explainable AI (XAI), cloud providers and users can obtain explanations of how AI models predict workload patterns, delineate potential bottlenecks, and offer the best configurations for resources. This kind of transparency promotes trust and effective oversight of cloud operations. This paper reviews a few of the most prevalent methods: SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), which are used to understand the factors that drive AI-based decisions for cloud optimization.