학술논문
DeepPR: Progressive Recovery for Interdependent VNFs With Deep Reinforcement Learning
Document Type
Periodical
Author
Source
IEEE Journal on Selected Areas in Communications IEEE J. Select. Areas Commun. Selected Areas in Communications, IEEE Journal on. 38(10):2386-2399 Oct, 2020
Subject
Language
ISSN
0733-8716
1558-0008
1558-0008
Abstract
The increasing demand for diverse network services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted progressively due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to the interdependence between virtual network functions and infrastructure elements. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some interdependencies exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.