A spectral clustering approach to network-aware virtual request partitioning
Abstract Virtual request partitioning is an essential subproblem of two common problems in virtual networks, namely, virtual network embedding (VNE) and virtual machine placement (VMP). In this study, we consider a network-aware variant of the problem where the objective is to partition a virtual request so as to minimize the total amount of inter-cluster traffic. This problem is equivalent to the (k, v)-balanced partitioning problem, an NP-complete problem. To handle the inherent complexity of this problem, we develop a spectral clustering-based partitioning scheme that produces good solutions in a reasonable amount of time. Our solution consists of several components: (a) spectral clustering, (b) a Constrained k-means partitioning algorithm that ensures that capacity limits for clusters are met, and for which we present a polynomial-time greedy algorithm, and (c) a greedy refinement algorithm using simulated annealing to further improve the clustering solution. Simulation results indicate that our algorithm outperforms existing partitioning schemes in terms of inter-cluster traffic minimization.