Computation-Aware Link Repair for Large-Scale Damage in Distributed Cloud Networks

Due to the distributed deployment and inter-network dependence, distributed cloud network (DCN) is vulnerable to large-scale damage, making emergent system recovery of vital importance. Given limited resources at an early stage of network recovery, we propose a computation-aware link repair (CALR) algorithm to meet the computation demands of data centers in heavily damaged DCNs. Taking into account both network structure and traffic dynamics, we formulate a total system cost minimization problem to guarantee network repair performance. To tackle this challenging mixed-integer programming problem, we leverage the Benders decomposition (BD) to transfer it into an iteration problem with the mutually independent master problem and subproblem, which are solved by the cutting plane and the minimum cost flow algorithms, respectively. To accelerate the convergence speed of the proposed BD-based approach, we apply a small perturbation on the subproblem for facilitating the recovery of large-scale networks. Moreover, the computational complexity is reduced significantly by generating maximal non-dominated Benders cuts. Numerical simulations demonstrate that the proposed approach outperforms benchmarks under different settings such as network scale, data significance, available resources, and topology.