Dynamic application placement and resource optimisation technique for heterogeneous fog computing environments

The increase in demand for reducing the latency in service requests of Internet of Things (IoT) applications has led researchers to drift from Cloud computing to Fog computing paradigms. Fog computing brings computing and data storage closer to devices and sensors, reducing latency and improving response time and reliability. However, implementing fog computing successfully requires fog nodes and applications to be deployed effectively to provide high-performance services. In addition, fog computing faces challenges in efficient resource scheduling due to the scarce capacity of fog nodes and the dynamic and heterogeneous nature of devices, leading to complexities in workload allocation and optimal resource utilisation. This work presents a simplified model for dynamically placing the application modules in a heterogeneous fog computing environment. A new framework for learning scheme is implemented using a Deep Deterministic Policy Gradient (DDPG)-based reinforcement learning technique for predicting the operations and determining the cumulative rewards. A test environment demonstrates that the proposed framework has lower mobility dependency, higher reward and reduced variance compared to existing schemes.