Deep learning inspired routing in ICN using Monte Carlo Tree Search algorithm

Abstract

Information Centric Networking (ICN) provides caching strategies to improve network performance based on consumer demands from the intermediate routers. It reduces the load on content server, network traffic, and improves end-to-end delay. The content requesters use an Interest packet containing the name of data to express their needs. If such Interest packets are routed efficiently, the end to end delay and throughput of the network could be improved further. This paper describes an efficient method of forwarding Interest packets to retrieve the requested content at the earliest possible time. Here the data source is found and considered as a single player game with content requester as its start state and location of the desired content as final or goal state. The Monte Carlo Tree Search (MCTS) algorithm is used for constructing the path from content requester to concerned data source. For performance evaluation, the proposed scheme is integrated with Leave Copy Down (LCD) and Leave Copy Everywhere (LCE), Cache Less for More (CL4M), and Probability based caching (ProbCache) In ns-3 simulation environment (ndnSim), all these are evaluated in terms of content search latency, server hit ratio, network load, overhead and throughput. Simulation observation reveals that the integration of MCTS significantly improves performance in regard to experimental parameters.