Quality of Service Performance of Multi-Core Broadband Network Gateways
PROCEEDINGS OF THE 8TH NETWORK TRAFFIC MEASUREMENT AND ANALYSIS CONFERENCE, TMA 2024(2024)
BISDN GmbH
Abstract
Broadband network access is typically managed by Broadband Network Gateways (BNGs), which can be implemented as a Virtual Network Function (VNF). This paradigm shift is caused by network softwarization and allows the BNG to be deployed on commodity hardware, significantly reducing capital expenditure (CAPEX). But packet processing operations and complex Quality of Service (QoS) policies make it difficult to provide low and predictable latency at scale for a large number of subscribers. To improve performance, parallel queues at the Network Interface Card (NIC) and multiple dedicated CPU cores for packet processing are used, processing 50 million packets per second on commodity x86 hardware. How to guarantee latency, however, remains unclear. In this study, we conducted testbed-based experiments on a VPP/DPDK implementation of the BNG to benchmark its performance. Our findings reveal how latency and its variation increase with background traffic, and we analyze a parameter that contributes to a trade-off between throughput and latency. We also examine the ability of the multi-core architecture to guarantee latency, at a cost of reduced port utilization. These observations influence the design goal of isolating subscriber traffic and highlight the suitability of software BNG for guaranteeing performance.
MoreTranslated text
Key words
Performance measurements,benchmarking,Quality of Service,DPDK,VPP,BNG
求助PDF
上传PDF
View via Publisher
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
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined