Toward a Reinforcement Learning Approach for Balancing Risk and Cost in Cloud-Based IoT-Driven Business Processes

Secure and cost-efficient deployment of Internet of Things (IoT)-driven business processes (BPs) in multicloud environments is a complex task, particularly under dynamic workloads and strict risk or budgetary constraints. We address this challenge by leveraging deep reinforcement learning (DRL) to determine optimal task-to-cloud allocations that comply with user-defined cost or risk thresholds. Our approach introduces a Q-learning model that integrates a confidentiality risk metric and a cost evaluation function into its learning process. Unlike traditional heuristics, the DRL agent adapts to evolving constraints through trial-and-error interaction with its environment. Experimental results on a diverse set of deployment configurations show that our approach achieves 25%–30% reductions in both risk and cost compared to heuristic baselines, while satisfying thresholds in 75% of cases. It also demonstrates strong adaptability to dynamic changes, including task additions and resource fluctuations. These findings highlight the potential of reinforcement learning for reliable, constraint-aware deployment in cloud-based IoT systems.