Pattern-Driven Resource Allocation in Optical Networks

The efficient allocation of network resources is key to the overall performance and the quality of services provided. Thanks to their high data rates, optical networks are the cornerstone of present and future core, metro, access, and datacenter networking. Many works in the field, formulate resource allocation operations as offline combinatorial problems, assuming a known static traffic matrix, and use integer linear programming (ILP) as well as heuristics. In contrast, other works assume randomly generated traffic and propose online schemes that serve connection requests one by one. In practice, traffic in optical networks is neither static nor completely random, but is usually semi-periodic, following some (e.g., daily or weekly) pattern. We present a traffic-pattern-driven approach for elastic optical networks for serving immediate and in advance network requests, where the decisions of an offline process, optimizing resource allocation for the traffic pattern expected during an epoch (day, week, etc.), are analyzed and then drive the operation of an online process that serves requests one by one, as they arrive. In this way, the online mechanism's decisions come close to the optimal ones, if the traffic pattern indeed repeats itself to some extent, while its execution time remains small. We present two alternatives of this approach, the exact and the relative, based on the way the offline mechanism's decisions are analyzed and translated to online actions. Our simulation results exhibit the performance benefits of the pattern-driven approach under various traffic conditions.