Dynamic and Adaptive Bandwidth Defragmentation in Spectrum-Sliced Elastic Optical Networks With Time-Varying Traffic

TLDR

Elastic optical networks provide agile bandwidth management in the optical layer. The study investigates dynamic, adaptive bandwidth defragmentation in elastic optical networks under time‑varying traffic via systematic connection reconfigurations. The authors design a four‑step defragmentation procedure that selects routing and spectrum assignment algorithms, builds a dependency graph for traffic migration, applies a move‑to‑vacancy method, and uses intelligent timing and adaptive defragmentation‑ratio selection to balance blocking probability and complexity. Simulations demonstrate that the DF‑AT‑AR algorithm achieves a superior tradeoff between blocking probability and operational complexity compared to existing methods.

Abstract

Elastic optical networks (EONs) enable network operators to have agile bandwidth management in the optical layer. In this paper, we investigate dynamic and adaptive bandwidth defragmentation (DF) in EONs with time-varying traffic using connection reconfigurations. We consider how to design DF procedure in a systematic way, and study the problems that have not been fully explored so far. Basically, we divide the procedure design into four subproblems: “How to reconfigure?,” “ How to migrate traffic?,” “When to reconfigure?,” and “What to reconfigure?,” and solve them sequentially. For “How to reconfigure?,” we investigate the combination of routing and spectrum assignment (RSA) algorithms for DF, i.e., the RSA algorithm that the connections are originally served with and the algorithm that they are re-optimized with. For “ How to migrate traffic?,” we propose to construct a dependency graph to represent the relations among the selected connections and to use it to assist the best-effort traffic migration. A move-to-vacancy method is also proposed to further reduce the traffic disruptions. For “When to reconfigure?” and “ What to reconfigure?,” we propose intelligent timing selection and adaptive DF ratio selection methods to tackle the tradeoff between the bandwidth blocking probability (BBP) performance and operational complexity. Simulation results show that the algorithm with both methods implemented (DF-AT-AR) achieves better tradeoff between BBP performance and operational complexity, when compared with existing algorithms.