DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing reliance upon cloud 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 in a progressive way 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 dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers 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 the near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.Comment: Technical Report, 12 page

Inter-domain traffic routing in vehicular delay tolerant networks

“Copyright © [2010] IEEE. Reprinted from IEEE International Conference on Communications (IEEE ICC 2010). ISSN:1550-3607. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.”In this paper, we consider the problem of dynamic inter-domain traffic routing between a VDTN and a non-DTN (e.g., Internet). The inter-domain traffic can be classified as inbound and outbound traffic. Our main contribution in this work is the intro- duction of several fault-tolerant routing algorithms for inbound and outbound traffic. Using simulations, we compare the performance of the proposed algorithms in terms of required resources, packet delivery time, and blocking probability.This work was supported in part by the Instituto de Telecomunicações, Next Generation Networks and Applications Group (NetGNA), Covilhã Delegation, Portugal in the framework of the VDTN@Lab Project