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分组聚合机制的性能分析及其在接入（例如 IoT、4G/5G）、核心和数据中心网络中的应用。

Performance Analysis of Packet Aggregation Mechanisms and Their Applications in Access (e.g., IoT, 4G/5G), Core, and Data Centre Networks.

机构信息

Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland.

Faculty of Automatic Control, Electronics and Computer Science, Department of Distributed Systems and Informatic Devices, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

出版信息

Sensors (Basel). 2021 Jun 4;21(11):3898. doi: 10.3390/s21113898.

DOI:10.3390/s21113898
PMID:34200090
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8201355/
Abstract

The transmission of massive amounts of small packets generated by access networks through high-speed Internet core networks to other access networks or cloud computing data centres has introduced several challenges such as poor throughput, underutilisation of network resources, and higher energy consumption. Therefore, it is essential to develop strategies to deal with these challenges. One of them is to aggregate smaller packets into a larger payload packet, and these groups of aggregated packets will share the same header, hence increasing throughput, improved resource utilisation, and reduction in energy consumption. This paper presents a review of packet aggregation applications in access networks (e.g., IoT and 4G/5G mobile networks), optical core networks, and cloud computing data centre networks. Then we propose new analytical models based on diffusion approximation for the evaluation of the performance of packet aggregation mechanisms. We demonstrate the use of measured traffic from real networks to evaluate the performance of packet aggregation mechanisms analytically. The use of diffusion approximation allows us to consider time-dependent queueing models with general interarrival and service time distributions. Therefore these models are more general than others presented till now.

摘要

大量由接入网络生成的小数据包通过高速互联网核心网络传输到其他接入网络或云计算数据中心，这带来了一些挑战，如吞吐量差、网络资源未充分利用和更高的能耗。因此，开发应对这些挑战的策略至关重要。其中之一是将较小的数据包聚合到更大的有效负载数据包中，这些聚合的数据包组将共享相同的头部，从而提高吞吐量、提高资源利用率和降低能耗。本文综述了分组聚合在接入网络（如物联网和 4G/5G 移动网络）、光核心网络和云计算数据中心网络中的应用。然后，我们提出了基于扩散逼近的新分析模型，用于评估分组聚合机制的性能。我们展示了使用来自真实网络的测量流量来对分组聚合机制进行分析评估。扩散逼近的使用允许我们考虑具有一般到达时间和服务时间分布的时变排队模型。因此，这些模型比迄今为止提出的其他模型更通用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/d97f30dae1cd/sensors-21-03898-g021.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/dab368edd11b/sensors-21-03898-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/cf5262e0609d/sensors-21-03898-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/ec866d5d084e/sensors-21-03898-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/c6e4a49472ca/sensors-21-03898-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/131064610612/sensors-21-03898-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/c4b59f8fe747/sensors-21-03898-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/a06979946d0d/sensors-21-03898-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/962f90971130/sensors-21-03898-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/e188fa3a7615/sensors-21-03898-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/41ec8c28c2e8/sensors-21-03898-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/0354ba54b5f3/sensors-21-03898-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/99f85c4683c9/sensors-21-03898-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/cf27f664c951/sensors-21-03898-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/edba95c62039/sensors-21-03898-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/adfbf1fe2471/sensors-21-03898-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/6b1f4636d025/sensors-21-03898-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/c2a3de360841/sensors-21-03898-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/d97f30dae1cd/sensors-21-03898-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/e7c4c6b0035f/sensors-21-03898-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/1b5d41abaf08/sensors-21-03898-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/597fb99010f0/sensors-21-03898-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/dab368edd11b/sensors-21-03898-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/cf5262e0609d/sensors-21-03898-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/ec866d5d084e/sensors-21-03898-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/c6e4a49472ca/sensors-21-03898-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/131064610612/sensors-21-03898-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/c4b59f8fe747/sensors-21-03898-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/a06979946d0d/sensors-21-03898-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/962f90971130/sensors-21-03898-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/e188fa3a7615/sensors-21-03898-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/41ec8c28c2e8/sensors-21-03898-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/0354ba54b5f3/sensors-21-03898-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/99f85c4683c9/sensors-21-03898-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/cf27f664c951/sensors-21-03898-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/edba95c62039/sensors-21-03898-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/adfbf1fe2471/sensors-21-03898-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/6b1f4636d025/sensors-21-03898-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/c2a3de360841/sensors-21-03898-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9226/8201355/d97f30dae1cd/sensors-21-03898-g021.jpg

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