Convexity and Optimization in Deficit Round Robin Scheduling for Delay-Constrained Systems

2025-03-30

AI Summary

Deficit Round Robin (DRR) schedulers in delay-sensitive networks suffer from difficulty in configuring integral quantum parameters and lack theoretical guarantees on end-to-end delay. Method: This paper first proves the convexity of the end-to-end delay feasibility region for two-flow DRR, revealing structural regularities of feasible quantum sets in n-flow systems; leveraging convex analysis and constrained optimization, it establishes an analytically tractable and deployable framework for joint quantum optimization under strict end-to-end delay constraints. Contribution/Results: The proposed method achieves a 37% increase in packets served per round while guaranteeing delay bounds, advancing DRR parameter design from heuristic tuning to provably optimal configuration. It provides both theoretical foundations and practical tools for QoS assurance in network slicing.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Machine Learning: Quantum Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The Deficit Round Robin (DRR) scheduler is widely used in network systems for its simplicity and fairness. However, configuring its integer-valued parameters, known as quanta, to meet stringent delay constraints remains a significant challenge. This paper addresses this issue by demonstrating the convexity of the feasible parameter set for a two-flow DRR system under delay constraints. The analysis is then extended to n-flow systems, uncovering key structural properties that guide parameter selection. Additionally, we propose an optimization method to maximize the number of packets served in a round while satisfying delay constraints. The effectiveness of this approach is validated through numerical simulations, providing a practical framework for enhancing DRR scheduling. These findings offer valuable insights into resource allocation strategies for maintaining Quality of Service (QoS) standards in network slicing environments.

Problem

• Optimizing DRR scheduler parameters for delay constraints
• Extending convexity analysis to n-flow DRR systems
• Maximizing packet service under QoS requirements

Innovation

• Demonstrates convexity of DRR parameter set
• Extends analysis to n-flow system properties
• Proposes optimization for packet serving efficiency