Paper Info
Title
DeepDRAMA: Deep Reinforcement Learning-based Disaster Recovery with Mitigation Awareness in EONs
Abstract
Elastic Optical Networks (EONs) have become a promising solution to satisfy the dramatic growth of bandwidth demand due to 5G and cloud applications. Due to the flexibility of resource allocation, EONs provide high spectrum utilization efficiency, and because of this, developing efficient policies to ensure the survivability of EONs is a challenging problem. A well-designed disaster management plan is needed to prevent data loss during network failures and large-scale disasters. The bottleneck problem caused by disabled parts of the network causes difficulties for disaster recovery. Depending on the disaster, even traffic that may be far away from the disaster may be impacted by it. In this paper, we propose a new approach to disaster management using machine learning to facilitate efficient recovery. In addition to traffic immediately affected by the disaster, all traffic which is "close to" the disaster is re-routed and re-assigned with possibly degraded service, while requests "far from" the disaster are left unaffected. A deep reinforcement learning disaster recovery algorithm with mitigation awareness (DeepDRAMA) is proposed for recovery. A novel deep reinforcement learning agent is designed and trained for the agent to select the appropriate level of service degradation for re-assigned traffic. Simulation results show the performance improvement with DeepDRAMA.
Year
DOI
Venue
2021
10.1109/GLOBECOM46510.2021.9685680
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
Keywords
DocType
ISSN
Disaster management, degraded service, machine learning
Conference
2334-0983
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Rujia Zou121.40
Nathaniel Bury200.34
Hiroshi Hasegawa31815.70
Masahiko Jinno401.35
Suresh Subramaniam51050124.36