Service-centric Segment Routing Mechanism using Reinforcement Learning for Encrypted Traffic

For the past decade, IP (Internet Protocol) routing approaches utilize TCAM (Ternary Content Addressable Memory) for the rule matching in the switches. These approaches are expensive and require more power consumption. Fortunately, the emerging of segment routing can resolve this drawback by encoding a routing path into the packet header to forward the packets to a destination. However, the standard segment routing algorithm has encountered a main problem. Using the shortest path to forward the packets can lead to a high traffic load on these paths and a performance reduction. It results in a decrease in user's perception and some negative economic impacts for ISPs (Internet Service Providers). Therefore, in this paper, we propose a novel service-centric segment routing mechanism using reinforcement learning in the context of encrypted traffic. Our proposal aims to help ISPs to decrease the influence of the network problems and meet the strict user's requirement related to QoE (Quality of Experience). The obtained results under the considered conditions demonstrate that our approach out-performs the standard segment routing algorithm and requires reasonable computational cost.