PROJECT TITLE :
Link Failure Recovery Over Large Arbitrary Networks: The Case of Coding
Network coding-based mostly link failure recovery techniques give close to-hitless recovery and offer high capacity efficiency. Diversity coding is the first technique to incorporate coding during this field and is straightforward to implement over small networks. However, the capacity potency of this implementation is restricted by its systematic coding and high style complexity despite having lower complexity than the other coding-based mostly recovery techniques. During this paper, we tend to propose a straightforward column generation-based style algorithm and a completely unique advanced diversity coding technique to realize close to-hitless recovery over massive networks. The traffic matrix, which consists of unicast affiliation demands, is decomposed into traffic vectors for each destination node. Further, the association demands in every traffic vector are partitioned into coding teams. The design framework consists of two components: a main problem and a subproblem. The main downside is solved with Linear Programming (LP) and Integer Linear Programming (ILP), whereas the subproblem can be solved with totally different methods. Simulation results recommend that the novel design algorithm simplifies the capability placement downside, that enables implementing diversity coding-based mostly recovery together with the novel coding structure on massive networks with arbitrary topology. It achieves close to-hitless recovery with an almost optimal capacity efficiency for any single destination-primarily based recovery.
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