Burst-Aware Mixed Flow Scheduling in Time-Sensitive Networks for Power Business

Jiming Yao,Lei Wang,Zheng Zhou,Liang Dong,Peng Zhan

2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC)（2023）

State Grid Smart Grid Research Institute Co.

Cited 0|Views7

Abstract

As power grid become more intelligent, it is critical to ensure efficient scheduling of all types of traffic to maintain system security and stability. Unexpected traffic generated by unforeseen events can also impact the normal operation of the network. Time-sensitive networks (TSN), which are deterministic network technologies, provide various traffic scheduling mechanisms that ensure the safe transmission of critical traffic. To address the challenges posed by bursty flows, we propose a hybrid traffic delivery mechanism that efficiently schedules bursty flows. This mechanism allocates a portion of bandwidth to the ATS mechanism, while scheduling time-sensitive flows and common bandwidth flows separately using the gated list of the TAS mechanism and the mapped time slot adjustment of the CQF mechanism. Our experiments demonstrate that the proposed BASM mechanism can schedule more flows with the same parameter settings than other mechanisms discussed in this paper, and can efficiently schedule burst flows with a success rate of over 94%. As a result, our mechanism enables efficient scheduling of mixed flows that include bursty flows.

Translated text

Key words

Time-Sensitive Network,Traffic Scheduling,Asynchronous Traffic Shaper,Cyclic-Queuing and Forwarding,Time-Aware Shaper

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper