Achieving QoS for Real-Time Bursty Applications over Passive Optical Networks

TL;DR

This paper introduces a novel, computationally-efficient bandwidth allocation policy for Passive Optical Networks that ensures strict QoS for bursty, real-time applications by using a Model Predictive Control approach, reducing delay violations.

Contribution

It proposes a far-sighted, control-theoretic bandwidth allocation strategy using MPC, specifically designed for bursty traffic and strict QoS in PONs, with polynomial-time implementation.

Findings

Reduces delay violations by approximately 15% compared to existing methods.

Provides a robust solution adaptable to varying traffic arrivals.

Models resource allocation as a Model Predictive Control problem.

Abstract

Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict Quality of Service (QoS) requirements. Passive Optical Network (PON) is a popular access network technology, which is envisioned to handle such applications at the access segment of the network. However, the existing standards cannot handle strict QoS constraints. The available solutions rely on instantaneous heuristic decisions and maintain QoS constraints (mostly bandwidth) in an average sense. Existing works with optimal strategies are computationally complex and are not suitable for uRLLC applications. This paper presents a novel computationally-efficient, far-sighted bandwidth allocation policy design for facilitating bursty traffic in a PON framework while satisfying strict QoS (age of information/delay and bandwidth) requirements of modern applications. To this purpose, first we design a delay-tracking mechanism which allows us to model the resource allocation problem from a control-theoretic viewpoint as a Model Predictive Control (MPC). MPC helps in taking far-sighted decisions regarding resource allocations and captures the time-varying dynamics of the network. We provide computationally efficient polynomial-time solutions and show its implementation in the PON framework. Compared to existing approaches, MPC reduces delay violations by approximately 15% for a delay-constrained application of 1ms target. Our approach is also robust to varying traffic arrivals.