Towards an Optimized Multi-Cyclic Queuing and Forwarding in Time Sensitive Networking with Time Injection

2025-06-27

AI Summary

Prior research on Multi-Cyclic Queuing and Forwarding (Multi-CQF) in Time-Sensitive Networking (TSN) suffers from insufficient configuration studies, limited support for heterogeneous traffic scheduling, and unexplored impacts of Time Injection (TI). Method: This paper introduces TI into the Multi-CQF architecture for the first time and proposes a configuration method integrating domain-knowledge-constrained modeling with a hybrid GA-GASA optimization algorithm—combining genetic algorithm (GA) global search and simulated annealing (SA) local refinement—to reduce search space and enhance local exploitation. Contribution/Results: Experiments demonstrate that, compared to baseline SA, the proposed method increases scheduling capacity for time-sensitive flows by 15% on average, accelerates convergence by 20%, and reduces time complexity. This work provides a scalable optimization framework and empirical validation for efficient Multi-CQF deployment in real-world TSN scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Cyclic Queuing and Forwarding (CQF) is a Time-Sensitive Networking (TSN) shaping mechanism that provides bounded latency and deterministic Quality of Service (QoS). However, CQF's use of a single cycle restricts its ability to support TSN traffic with diverse timing requirements. Multi-Cyclic Queuing and Forwarding (Multi-CQF) is a new and emerging TSN shaping mechanism that uses multiple cycles on the same egress port, allowing it to accommodate TSN flows with varied timing requirements more effectively than CQF. Despite its potential, current Multi-CQF configuration studies are limited, leading to a lack of comprehensive research, poor understanding of the mechanism, and limited adoption of Multi-CQF in practical applications. Previous work has shown the impact of Time Injection (TI), defined as the start time of Time-Triggered (TT) flows at the source node, on CQF queue resource utilization. However, the impact of TI has not yet been explored in the context of Multi-CQF. This paper introduces a set of constraints and leverages Domain Specific Knowledge (DSK) to reduce the search space for Multi-CQF configuration. Building on this foundation, we develop an open-source Genetic Algorithm (GA) and a hybrid GA-Simulated Annealing (GASA) approach to efficiently configure Multi-CQF networks and introduce TI in Multi-CQF to enhance schedulability. Experimental results show that our proposed algorithms significantly increase the number of scheduled TT flows compared to the baseline Simulated Annealing (SA) model, improving scheduling by an average of 15%. Additionally, GASA achieves a 20% faster convergence rate and lower time complexity, outperforming the SA model in speed, and efficiency.

Problem

• Optimizing Multi-Cyclic Queuing for diverse TSN traffic timing
• Exploring Time Injection impact on Multi-CQF scheduling efficiency
• Developing GA and GASA algorithms for Multi-CQF configuration

Innovation

• Multi-Cyclic Queuing Forwarding with multiple cycles
• Genetic Algorithm hybrid GA-Simulated Annealing approach
• Time Injection enhances Multi-CQF schedulability