Optimal Algorithms for Near-Hitless Network Restoration via Diversity Coding

Diversity coding is a network restoration technique which offers near-hitless restoration, while other state-of-the art techniques are significantly slower. Furthermore, the extra spare capacity requirement of diversity coding is competitive with the others. Previously, we developed heuristic algorithms to employ diversity coding structures in networks with arbitrary topology. This paper presents two algorithms to solve the network design problems using diversity coding in an optimal manner. The first technique pre-provisions static traffic whereas the second technique carries out the dynamic provisioning of the traffic on-demand. In both cases, diversity coding results in smaller restoration time, simpler synchronization, and much reduced signaling complexity than the existing techniques in the literature. A Mixed Integer Programming (MIP) formulation and an algorithm based on Integer Linear Programming (ILP) are developed for pre-provisioning and dynamic provisioning, respectively. Simulation results indicate that diversity coding has significantly higher restoration speed than Shared Path Protection (SPP) and p-cycle techniques. It requires more extra capacity than the p-cycle technique and SPP. However, the increase in the total capacity is negligible compared to the increase in the restoration speed.Comment: An old version of this paper is submitted to IEEE Globecom 2012 conferenc

Link Failure Recovery Over Large Arbitrary Networks: The Case of Coding

Abstract-Network coding-based link failure recovery techniques provide near-hitless recovery and offer high capacity efficiency. Diversity coding is the first technique to incorporate coding in this field and is easy to implement over small networks. However, the capacity efficiency of this implementation is restricted by its systematic coding and high design complexity despite having lower complexity than the other coding-based recovery techniques. In this paper, we propose a simple column generation-based design algorithm and a novel advanced diversity coding technique to achieve near-hitless recovery over large networks. The traffic matrix, which consists of unicast connection demands, is decomposed into traffic vectors for each destination node. Further, the connection demands in each traffic vector are partitioned into coding groups. The design framework consists of two parts: a main problem and a subproblem. The main problem is solved with Linear Programming (LP) and Integer Linear Programming (ILP), whereas the subproblem can be solved with different methods. Simulation results suggest that the novel design algorithm simplifies the capacity placement problem, which enables implementing diversity coding-based recovery including the novel coding structure on large networks with arbitrary topology. It achieves near-hitless recovery with an almost optimal capacity efficiency for any single destination-based recovery