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.