Efficient Adaptive Bandwidth Allocation for Deadline-Aware Online Admission Control in Time-Sensitive Networking

TL;DR

This article describes elsarticle.cls, a LaTeX document class tailored for formatting submissions to Elsevier journals. It presents a reworked class built on article.cls to minimize package conflicts, with extensive compatibility for natbib, AMS math packages, and common front matter components. The work details optional submission formats (preprint, final, single- or two-column) and robust front matter handling, including abstracts and keywords, to ensure consistent journal appearance. It also contrasts elsarticle.cls with the older elsart.cls and provides practical installation guidance via Elsevier resources and CTAN, facilitating reliable and streamlined manuscript preparation.

Abstract

With the growing demand for dynamic real-time applications, online admission control for time-critical event-triggered (ET) traffic in Time-Sensitive Networking (TSN) has become a critical challenge. The main issue lies in dynamically allocating bandwidth with real-time guarantees in response to traffic changes while also meeting the requirements for rapid response, scalability, and high resource utilization in online scenarios. To address this challenge, we propose an online admission control method for ET traffic based on the TSN/ATS+CBS (asynchronous traffic shaper and credit-based shaper) architecture. This method provides a flexible framework for real-time guaranteed online admission control, supporting dynamic bandwidth allocation and reclamation at runtime without requiring global reconfiguration, thus improving scalability. Within this framework, we further integrate a novel strategy based on network calculus (NC) theory for efficient and high-utilization bandwidth reallocation. On the one hand, the strategy focuses on adaptively balancing residual bandwidth with deadline awareness to prevent bottleneck egress ports, thereby improving admission capacity. On the other hand, it employs a non-trivial analytical result to reduce the search space, accelerating the solving process. Experimental results from both large-scale synthetic and realistic test cases show that, compared to the state-of-the-art, our method achieves an average 56% increase in admitted flows and an average 92% reduction in admission time. Additionally, it postpones the occurrence of bottleneck egress ports and the first rejection of admission requests, thereby enhancing adaptability.