MVE-based Reinforcement Learning Framework with Explainability for improving Quality of Experience of Application Placement in Fog Computing

Fog computing can process big data generated by the IoT (IoT) architectures. The hierarchical, heterogeneous and distributed form of fog computing makes the application placement a challenging task. IoT applications are time-sensitive, and their placement decision is dependent on the user's Quality of Experience (QoE). This paper proposes an explainable Model Value Evaluation based Reinforcement Learning (MVERL) framework for placing applications among appropriate fog nodes. The quality of the application placement policies is good in terms of metrics related to quality like correctness, model relevance, $\in$-differential privacy, and robustness. The performance results of the proposed MVERL are evaluated considering fog nodes with both limited and unlimited processors. The simulation found that the proposed MVERL outperforms existing works concerning a few performance metrics.