A Deep Reinforcement Learning (DRL) Based Approach to SFC Request Scheduling in Computer Networks

study investigates the use of Deep Reinforcement Learning (DRL) to minimize the latency between the source and destination of Service Function Chaining (SFC) requests in Neural Networks. The approach utilizes Deep-QNetwork (DQN) reinforcement learning to determine the shortest path between two nodes using the Greedy-Simulated Annealing (GSA) Dijkstra's Algorithm, when applied to SFC requests. The containers within the SFC framework help train the RL model based on bandwidth restrictions (fiber networks) to optimize the different pathways in terms of action space. Through rigorous evaluation of varying action spaces in models, we assessed that the Dijikstra's Algorithm, within the sphere, is in fact a viable optimized solution to SFC request based problems. Our findings illustrate how this framework can be applied to early request based topologies to introduce a more optimized method of resource allocation, quality of service, and network performance to generalize the relationship between SFC and RL.