Dynamic Bandwidth Allocation for PON Slicing with Performance-Guaranteed Online Convex Optimization

TL;DR

This paper introduces a learning-based online convex optimization algorithm for PON network slicing that dynamically allocates bandwidth to reduce latency while ensuring fairness and responsiveness to demand.

Contribution

It presents a novel slice-aware, demand-responsive DBA algorithm for PONs based on online convex optimization, improving latency and fairness.

Findings

Reduces average latency for delay-sensitive ONUs

Guarantees minimal bandwidth to all ONUs

Outperforms traditional static allocation methods

Abstract

The emergence of diverse network applications demands more flexible and responsive resource allocation for networks. Network slicing is a key enabling technology that provides each network service with a tailored set of network resources to satisfy specific service requirements. The focus of this paper is the network slicing of access networks realized by Passive Optical Networks (PONs). This paper proposes a learning-based Dynamic Bandwidth Allocation (DBA) algorithm for PON access networks, considering slice-awareness, demand-responsiveness, and allocation fairness. Our online convex optimization-based algorithm learns the implicit traffic trend over time and determines the most robust window allocation that reduces the average latency. Our simulation results indicate that the proposed algorithm reduces the average latency by prioritizing delay-sensitive and heavily-loaded ONUs while guaranteeing a minimal window allocation to all ONUs.