A perturbation algorithm for embedding virtual data centers in multipath networks

Network virtualization supports predicted network performance for applications by providing tenants with a virtual data center in multi-tenant data centers. The Hose model was recently extended for the virtual network abstraction and deployed in an Oktopus system, which offers the trade-off between cost and network performance. Embedding algorithms based on Oktopus model were well studied in the single-root tree topology. In this paper, we investigate congestion-aware allocation of virtual data centers in multipath networks. First, we formulate bandwidth constraints with linear programming, and provide a complete solution with exhaustive searching. Next, to reduce time complexity, we propose a perturbation algorithm to VM placement, which detects the bandwidth bottleneck of the current virtual machine placement and then adjusts the assignment to reduce congestion. The perturbation algorithm is compatible with both load-balanced and single-path routing algorithms. We compare the performance of the exhaustive searching algorithm and perturbation algorithm with load-balanced or single-path routing by simulations. The perturbation algorithm with load-balanced routing performs close to the exhaustive searching algorithm while significantly reduces the time complexity. Therefore, it offers a good tradeoff between time complexity and network performance.