Resource Provisioning in Fog Computing through Deep Reinforcement Learning

The massive growth of connected devices has made traditional cloud systems inadequate to sustain the scalability, mobility, and heterogeneous nature of the Internet of Things (oT). Distributed clouds have become a potential business opportunity for many service providers enabling the deployment of services on computational resources from the cloud up to the edge. However, challenges persist in fog-cloud infrastructures. One of them is known as Service Function Chaining (SFC), where providers benefit from network softwarization to create virtual chains of connected micro-services. Research has tackled SFC Allocation (SFCA) through theoretical modeling and heuristic algorithms, which often cannot cope with the dynamic behavior of the network. Recent works have addressed these challenges through Machine Learning (ML), which can be capable of dynamically reconfiguring cloud-native service requirements over the continuum of virtual resources in next-generation networks. Thus, in this paper, a Deep Reinforcement Learning (DRL) approach is proposed for SFCA in Fog Computing focused on energy efficiency. Our agent learns about the best resource allocation decisions, focused on reducing costs from a previously presented Mixed-integer linear programming (MILP) formulation. Results show that our agent achieves comparable performance to state-of-the-art MILP formulations during dynamic use cases, obtaining 95% of request acceptance.