Multiple Constrained Routing Algorithms in Large-Scaled Software Defined Networks

TL;DR

This paper introduces a heuristic algorithm for large-scale software defined networks that efficiently maximizes demand throughput while respecting bandwidth, delay, and hop constraints, outperforming existing methods in speed and capacity.

Contribution

The paper presents a novel heuristic algorithm for constrained routing in large SDNs that improves throughput and runtime compared to existing solutions.

Findings

Achieves over 90% of total demand bandwidth within 10 seconds.

Outperforms existing algorithms in throughput and speed.

Parallelizable algorithm significantly speeds up processing.

Abstract

In this paper, we consider the bandwidth-delay-hop constrained routing problem in large-scaled software defined networks. A number of demands, each of which specifies a source vertex and a sink vertex, are required to route in a given network. We are asked to select a subset of demands, and assign a routing path for each selected demand without violating the hop and delay constraints, while assuring that the bandwidth occupied in each edge is not beyond its capacity. The goal is to maximize the throughput (the total bandwidth) of the selected demands. We develop an efficient heuristic algorithm for the problem, which consists of three main steps, namely, computing feasible paths for each demand, sorting the demands with some priority rules, selecting a path for each demand. The algorithm is tested with networks of actual sizes and topologies, generated by Huawei Technologies Company. The experiments show that the proposed approach outperforms existing algorithms both in throughput and in running time. In the experiments, our algorithm achieves more than 90\% of the total bandwidth of the given demands within 10 seconds. Moreover, a large part of our algorithm can run in parallel which largely speeds up the process when using multi-core processors.