Optimizing Forwarding Strategies in Named Data Networking Using Reinforcement Learning
Zhafirah Naghmah Ahmad, Fika Triana, R. Betshrine Rachel, Ridha Muldina Negara, Ratna Mayasari, Sri Astuti, Syamsul Rizal · 2023
In the current network architecture, IP addresses are used, where data transmission uses the host address on each device. From this data delivery method, NDN emerges as a new paradigm in data transmission from being host-centric to becoming data-centric. There is a strategy used in research with the weakness of congestion in the forwarding strategy. Therefore, modeling the forwarding strategy using Reinforcement Learning is designed to overcome this problem. In the run simulation, an environment will be created in the Reinforcement Learning system with several scenarios in the NDN network. To measure the success of the system, testing is carried out to achieve maximum results, such as the Reinforcement Learning process, which is trial and error in nature, which means that several experiments are carried out, such as the exploration process carried out by the agent in the environment to achieve the goal and get the expected maximum reward. The components used in Reinforcement Learning in the training process are agents, actions, policies, and rewards. The tests aim to make NDN an efficient network system, simplify network performance automatically using Reinforcement Learning, and make NDN a network system that can overcome congestion for forwarding.