DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

TL;DR

This paper introduces DeepPR, a deep reinforcement learning-based method for progressive recovery of interdependent virtual network functions, effectively prioritizing recovery tasks under limited resources during large-scale failures.

Contribution

It presents a novel Deep RL approach for the NP-hard progressive recovery problem in networks with VNFs and dependencies, demonstrating near-optimal solutions and robustness.

Findings

DeepPR achieves near-optimal recovery sequences in simulations.

DeepPR outperforms baseline heuristics in robustness against adversarial failures.

The recovery problem is proven NP-hard, highlighting its complexity.

Abstract

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.