An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things

• URL: http://arxiv.org/abs/2207.08226v1
• Date: Sun, 17 Jul 2022 16:52:51 GMT
• Title: An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things
• Authors: Yinzhi Lu, Liu Yang, Simon X. Yang, Qiaozhi Hua, Arun Kumar Sangaiah, Tan Guo, Keping Yu
• Abstract summary: Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. Non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization.
• Score: 19.277349546331557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then a non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization.

Related papers

• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• SafeTail: Efficient Tail Latency Optimization in Edge Service Scheduling via Computational Redundancy Management [2.707215971599082]
  Emerging applications, such as augmented reality, require low-latency computing services with high reliability on user devices. We introduce SafeTail, a framework that meets both median and tail response time targets, with tail latency defined as latency beyond the 90th percentile threshold.
  arXiv  Detail & Related papers  (2024-08-30T10:17:37Z)
• Latency-aware Unified Dynamic Networks for Efficient Image Recognition [72.8951331472913]
  LAUDNet is a framework to bridge the theoretical and practical efficiency gap in dynamic networks. It integrates three primary dynamic paradigms-spatially adaptive computation, dynamic layer skipping, and dynamic channel skipping. It can notably reduce the latency of models like ResNet by over 50% on platforms such as V100,3090, and TX2 GPUs.
  arXiv  Detail & Related papers  (2023-08-30T10:57:41Z)
• Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning [10.223526707269537]
  Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services. In this paper, we investigate the collaborative inference problem in industrial IoT networks.
  arXiv  Detail & Related papers  (2022-12-31T05:53:17Z)
• Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications [87.05763097471487]
  This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading.
  arXiv  Detail & Related papers  (2022-11-24T20:13:17Z)
• Real-Time GPU-Accelerated Machine Learning Based Multiuser Detection for 5G and Beyond [70.81551587109833]
  nonlinear beamforming filters can significantly outperform linear approaches in stationary scenarios with massive connectivity. One of the main challenges comes from the real-time implementation of these algorithms. This paper explores the acceleration of APSM-based algorithms through massive parallelization.
  arXiv  Detail & Related papers  (2022-01-13T15:20:45Z)
• A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory [8.864453148536057]
  3IoT introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart internet-of-things (mMTC) applications. We propose a novel FUG allocation based on support machine scheduler (SVM) Second, LSTM architecture is used for traffic prediction and correction techniques to overcome prediction errors.
  arXiv  Detail & Related papers  (2021-08-02T11:33:02Z)
• Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling [60.48359567964899]
  We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay. We use a policy gradient based reinforcement learning algorithm that produces a scheduler that performs better than the available atomic policies.
  arXiv  Detail & Related papers  (2021-05-01T10:18:34Z)
• EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference [82.1584439276834]
  Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. We present EdgeBERT, an in-depth algorithm- hardware co-design for latency-aware energy optimization for multi-task NLP.
  arXiv  Detail & Related papers  (2020-11-28T19:21:47Z)
• Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines [9.585931043664363]
  We propose a dynamic compression ratio selection scheme for edge inference system with hard deadlines. Information augmentation that retransmits less compressed data of task with erroneous inference is proposed to enhance the accuracy performance. Considering the wireless transmission errors, we further design a retransmission scheme to reduce performance degradation due to packet losses.
  arXiv  Detail & Related papers  (2020-05-25T17:11:53Z)
• Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods [3.723835690294061]
  A novel intelligent bandwidth allocation scheme in NG-EPON using reinforcement learning is proposed and demonstrated for latency management. We verify the capability of the proposed scheme under both fixed and dynamic traffic loads scenarios to achieve 1ms average latency.
  arXiv  Detail & Related papers  (2020-01-21T18:58:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.