在工业物联网中利用主动队列管理协助关键任务流量
Assisting Mission-Critical Traffic Flows with Active Queue Management in Industrial Internet of Things
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中文总结 AI 辅助
研究工业物联网中关键任务流量问题，提出用主动队列管理协助，经实验证明多队列AQM方案能带来流隔离和容量共享优势，显著提升关键任务流量性能并给出部署建议。
中文摘要 AI 辅助
关键任务的工业物联网（IIoT）流量需要有限的网络延迟和抖动保证，以确保关键工业基础设施的安全运行。这些流量通常通过具有传统先进先出（FIFO）缓冲区的商用网络路由器进行通信。FIFO已被证明是众所周知的缓冲区膨胀现象的罪魁祸首，而主动队列管理（AQM）方案的部署已在IT领域的互联网上显示出对延迟敏感应用的显著性能提升。然而，缓冲区膨胀现象和AQM方案的有效性在基于IIoT的OT领域尚未得到研究。本文提出使用AQM作为一种轻量级且非侵入性的机制，以协助IIoT网络中的关键任务流量。我们的实验结果表明，多队列AQM方案提供了显著的流隔离和容量共享优势，并在网络压力下显著提高了关键任务流量的性能。我们还根据实验见解提供了部署建议。
英文摘要
Mission-critical Industrial Internet of Things (IIoT) traffic flows require bounded network latency and jitter guarantees to ensure the safe functioning of critical industrial infrastructure. These flows are typically communicated via commodity network routers with conventional First-In-First-Out (FIFO) buffers. FIFO has proven to be the culprit of the well-known bufferbloat phenomenon, and the deployment of Active Queue Management (AQM) schemes have demonstrated significant performance improvements for latency-sensitive applications over the Internet in the IT domain. However, the bufferbloat phenomenon and the efficacy of AQM schemes have not been studied in IIoT-based OT domain. In this paper, we propose the use of AQM as a lightweight and non-intrusive mechanism for assisting mission-critical traffic flows in IIoT networks. Our experimental results demonstrated that multi-queue AQM schemes provide substantial flow isolation and capacity sharing benefits, and significantly improve the performance of mission-critical traffic flows under network pressure. We further provide deployment recommendations based on our experimental insights.
