On the Average Cost and Latency of Migration to the Next Generation of Networks

Abstract

Networks are frequently changing due to new technologies. To increase the network performance, companies migrate their existing network to a network with a new technology. Finding an efficient optimization algorithm is an important challenge in the network migration. In this paper, the network migration problem is considered as a set of circuit migration problems in which multiple technicians simultaneously migrate the endpoints of circuits in order to minimize the average latency and average technician travel cost. While average latency indicates how fast the sites can be upgraded, average travel cost estimates the required cost for modernizing the network. First, We derive binary linear program and binary quadratic program formulations for average latency and average technician travel cost, respectively. Then we use the linear scalarization method to obtain a multi-objective optimization problem for simultaneously minimizing both costs. Our approach for solving the derived multi-objective optimization problem is based on converting it to a quadratic unconstrained binary optimization problem (QUBO) using the penalty method. Subsequently, we exploit the third generation of Fujitsu Digital Annealer which is a hybrid system of hardware and software to minimize the derived QUBO. To investigate the performance of our proposed method, we study extensive network migration instances on the 75-node CONUS network topology. Simulation results indicate that both costs can efficiently be optimized using our proposed method. We also directly solve the obtained multi-objective optimization problem with Gurobi solver. The comparison results show that our proposed method outperforms the Gurobi solver.