Optimized Service Function Deployment in Edge Computing Networks Using Deep Reinforcement Learning

The deployment of service function chains (SFCs) in integrated edge Computing networks presents significant challenges, including optimizing resource utilization, minimizing latency, and reducing energy consumption. These challenges arise from the dynamic nature of workloads, varying network conditions, and complex service function dependencies. Conventional optimization approaches often struggle to handle these complexities, as they depend on static models and assumptions that may not apply in practical situations. This paper introduces an innovative solution using deep reinforcement learning (DRL) to overcome these limitations. By modeling the deployment problem as a Markov decision process, we utilize the DRL's capability to learn optimal policies through iterative trial and error interactions with the environment. Our method adaptively adjusts service function placement in real time, accounting for workload variability, service dependencies, and network conditions. Comprehensive simulations demonstrate that our proposed approach achieves significant improvements in terms of latency reduction, resource utilization, and energy consumption compared to conventional methods, and can provide a new solution for SFC deployment.