A Hierarchical Constructive Heuristic for Large-Scale Survivable Traffic Grooming Problem under Double-Link Failures

TL;DR

This paper introduces a hierarchical heuristic for large-scale optical network routing that ensures survivability against double-link failures, significantly improving solution quality within practical computational limits.

Contribution

It presents a novel hierarchical constructive heuristic tailored for large-scale survivable traffic grooming, incorporating optimization and parallel computing techniques.

Findings

Improves solution quality by 18.5% on average

Efficiently handles networks with up to 2,600 nodes

Operates within one-hour time limit with limited memory

Abstract

This paper studies a survivable traffic grooming problem in large-scale optical transport networks under double-link failures (STG2). Each communication demand must be assigned a route for every possible scenario involving zero, one, or two failed fiber links. Protection against double-link failures is crucial for ensuring reliable telecommunications services while minimizing equipment costs, making it essential for telecommunications companies today. However, this significantly complicates the problem and is rarely addressed in existing studies. Furthermore, current research typically examines networks with fewer than 300 nodes, much smaller than some emerging networks containing thousands of nodes. To address these challenges, we propose a novel hierarchical constructive heuristic for STG2. This heuristic constructs and assigns routes to communication demands across different scenarios by following a hierarchical sequence. It incorporates several innovative optimization techniques and utilizes parallel computing to enhance efficiency. Extensive experiments have been conducted on large-scale STG2 instances provided by our industry partner, encompassing networks with 1,000 to 2,600 nodes. Results demonstrate that within a one-hour time limit and a 16 GB memory limit set by the industry partner, our heuristic improves the objective values of the best-known solutions by 18.5\% on average, highlighting its significant potential for practical applications.