LAUSR

The optical datacenter networks need periodical reconfiguration in response to traffic change. Before the networks reconfigure, the tenant requests are given and should be served within fixed transfer time and spectrum capacity. In this paper, the planning problem of serving the requests in optical orthogonal frequency division multiplexing datacenter networks is investigated. We introduce the knapsack-based spectrum and time allocation (KSTA) problem. The objective of this paper is to maximize the network throughput. We formulate the KSTA problem as an integer linear programming (ILP) model. However, ILP cannot find the optimal solution for large input requests within shorter time. To solve the problem, three fast heuristic algorithms, i.e., the most spectrum first, the most time first, and the most data volume first, are proposed to achieve suboptimal solutions. Furthermore, the simulated annealing (SA) algorithm is employed to yield a better suboptimal solution. The simulation results indicate that ILP provides an optimal solution for small input requests, whereas the three heuristic algorithms and SA can yield suboptimal solutions for large input requests. The results also show that the suboptimal solution to SA is better than those provided by the three heuristic algorithms.