Fair, Efficient and Low-Latency Packet Scheduling Using Nested Deficit Round Robin

Cited 79|Views5

Abstract

In the emerging high-speed integrated-services packet-switched networks, packet scheduling algorithms in switches and routers play a critical role in providing the quality of-service (QoS) guarantees required by many applications. We present a new scheduling discipline called nested deficit round robin (Nested-DRR), which is fair, efficient and in addition has a low latency bound. Nested-DRR splits each DRR round into one or more smaller rounds, within each of which we run a modified version of the DRR scheduling discipline. In this paper, we analytically prove that Nested-DRR results in a significant improvement in the latency bound in comparison to DRR, and in addition preserves the good properties of DRR such as the per-packet work complexity of O(1). Nested DRR also has the same relative fairness bound as DRR.

Translated text

Key words

delays,packet switching,quality of service,telecommunication network routing,DRR scheduling discipline,Nested-DRR results,QoS,efficient packet scheduling,fair packet scheduling,frame-based DRR scheduler,high-speed networks,integrated-services packet-switched networks,latency bound,low-latency packet scheduling,nested deficit round robin,packet scheduling algorithms,quality of-service,routers,switches

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

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper