Improving Network Availability with Low Network Construction Cost through Deep Reinforcement Learning

• Published in: International Conference on Information and Communication Technologies for Disaster Management (Online) pp. 1 - 5
• Main Authors: Cho, Choong-hee, Lee, Hyunho, Kim, Taeyoung, Ryoo, Jeong-dong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: deep reinforcement learning; disaster recovery; network availability; telecommunication
• ISSN: 2643-6868
• DOI: 10.1109/ICT-DM47966.2019.9032905

A carrier suffering a communication disaster may be unable to maintain network services using its own protection resources. In this case, it could avoid service disruptions by using other carriers' resources. However, along with national regulatory efforts requiring to coordinate multiple carriers' use of other carriers' resources, many technical issues exist in connecting two networks owned by different carriers. A key technical issue is determining where to connect the two networks. This paper introduces a new optimization problem whose objective is to minimize the costs of connecting two networks while improving overall network availability. To solve this NP-hard problem, we propose a deep reinforcement learning algorithm and compare its performance with that of a general greedy algorithm to evaluate the construction cost savings achieved while satisfying the target availability of each network.