Token Bucket-based Throughput Constraining in Cross-layer Schedulers

TL;DR

This paper introduces a low-complexity token bucket-based algorithm to enforce upper and lower service rate bounds in cross-layer schedulers, improving QoS adherence with minimal complexity.

Contribution

The paper proposes a novel, easily integrable algorithm that modifies utility function weights to constrain service rates in cross-layer schedulers, enhancing QoS guarantees.

Findings

Bounds service rate within about five slots for most schedulers

Effective for schedulers with stable weights

Less effective for highly volatile weight schedulers

Abstract

In this paper we consider upper and lower constraining users' service rates in a slotted, cross-layer scheduler context. Such schedulers often cannot guarantee these bounds, despite the usefulness in adhering to Quality of Service (QoS) requirements, aiding the admission control system or providing different levels of service to users. We approach this problem with a low-complexity algorithm that is easily integrated in any utility function-based cross-layer scheduler. The algorithm modifies the weights of the associated Network Utility Maximization problem, rather than for example applying a token bucket to the scheduler's output or adding constraints in the physical layer. We study the efficacy of the algorithm through simulations with various schedulers from literature and mixes of traffic. The metrics we consider show that we can bound the average service rate within about five slots, for most schedulers. Schedulers whose weight is very volatile are more difficult to constrain.