Distance-Adaptive Transmission in Cloud-Ready Elastic Optical Networks

In this paper, we analyze distance-adaptive transmission in cloud-ready elastic optical networks (EONs). To this end, we focus on the routing, modulation, and spectrum allocation problem, which we formulate as an integer linear programming problem, taking into account such optimization criteria (network performance metrics) as network capital expenditure and operating expenditure network cost, power consumption, and spectrum usage. The experiments are carried out with two representative network topologies and realistic data traffic models built on Cisco traffic predictions. The aim of this study is threefold. First, we examine the impact of distance-adaptive modulation formats on network performance. Second, we analyze how the optimization of a particular criterion influences other performance metrics. Finally, we focus on the benefits of using anycasting instead of typical unicast transmission in EONs. The results show that the use of multiple modulation formats instead of one selected format can bring significant savings in terms of all performance metrics (up to 40% for spectrum usage).