A taxonomy for congestion control algorithms in packet switching networks

Yang, Cui-Qing; Reddy, A. Veerabhadra

doi:10.1109/65.397042

Cited by 123 publications

(6 citation statements)

References 34 publications

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“…Digital massification and the risk of societal accommodation to data congestion in smart cities are part of an ongoing historical evolution of constantly increasing demands on data volume, transmission, and technological innovation within ultimately finite physical boundaries. Our historical perspective reveals that avoiding and controlling data congestion in digital networks has been a major concern since the first series of congestion collapses on the internet in October 1986 (Jacobson, 1988) and the rise of congestion control algorithms in the early 1990s (Yang & Reddy, 1995). Thirty‐five years later, the problem persists, though at a higher level.…”

Section: Historical and Relational Analogy: Massification And Congest...mentioning

confidence: 99%

Smart cities at risk: Systemic risk drivers in the blind spot of long‐term governance

Ottenburger

Ufer

2023

Risk Analysis

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In this article, we analyze “digital massification” in smart cities, that is, an ever‐growing number of market participants, consumers, and Internet of Things devices with simultaneous accommodation of users to increasing disturbances and inconveniences due to data congestion—as a driver for systemic risk. We argue that digital massification phenomena largely escape societal awareness due to their protracted evolution and are therefore still in the blind spot of long‐term governance. Our analysis makes methodological use of historical and relational analogy, and we introduce and elaborate concepts and terms that allow us to discuss the evolutionary nature of massification, that is, the foreseeable increasing probability of the occurrence of trigger events. Using the analogy to the history of road traffic congestion, we deduce that digital massification will most likely lead to a future “risk transition” where tolerated disturbances and inconveniences of the present will turn into systemic impacts. This insight calls for heightened sensitivity in governance to massification phenomena to ensure the long‐term resilience of smart cities.

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Section: Historical and Relational Analogy: Massification And Congest...mentioning

confidence: 99%

Smart cities at risk: Systemic risk drivers in the blind spot of long‐term governance

Ottenburger

Ufer

2023

Risk Analysis

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“…Untuk itu telah dilakukan berbagai penelitian untuk memperoleh hasil terbaik. Dari referensi [4], berikut ini merupakan taksonomi kendali kongesti.…”

Section: Metode Penelitianunclassified

Sistem Kendali Kongesti Di Internet

Rusmin

Machbub

Harsoyo

et al. 2010

MST

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AbstrakKongesti di internet terjadi karena jumlah pengiriman data melebihi kapasitas router yang ada. Namun demikian, kenyataannya tidak setiap kongesti yang terjadi disebabkan karena hal tersebut. Sebagai contoh ketika kecepatan data dari satu pengirim jauh lebih tinggi dari pengirim yang lain karena mekanisme umpan balik yang tidak tepat, maka pengirim dengan kecepatan data jauh lebih rendah dapat dikatakan mengalami kongesti. Saat ini kongesti diatasi dengan melibatkan 2 mekanisme pengendalian, yaitu flow/congestion control di sumber pengirim data dan Active Queue Management (AQM) di router. AQM bertugas memberikan umpan balik kepada sumber pengirim sebagai indikasi dari tingkat kongesti di router. Sumber pengirim akan mengirimkan data ke jaringan sesuai dengan umpan bailk tersebut. Mekanisme ini dirasakan masih belum cukup untuk menuntaskan persoalan kongesti di internet. Untuk itu, dalam makalah ini akan dipaparkan sebab-sebab terjadinya kongesti, kelemahan dan teknik kendali kongesti, yang telah dikembangkan sampai saat ini. Untuk memperoleh gambaran respon sistem, digunakan Matlab untuk simulasi sistem. AbstractInternet Congestion Control System. Internet congestion occurs when resource demands exceeds the network capacity. But, it is not the only reason. Congestion can happen on some users because some others user has higher sending rate. Then some users with lower sending rate will experience congestion. This partial congestion is caused by inexactly feedback. At this moment congestion are solved by the involvement of two controlling mechanisms. These mechanisms are flow/congestion control in the TCP source and Active Queue Management (AQM) in the router. AQM will provide feedback to the source a kind of indication for the occurrence of the congestion in the router, whereas the source will adapt the sending rate appropriate with the feedback. These mechanisms are not enough to solve internet congestion problem completely. Therefore, this paper will explain internet congestion causes, weakness, and congestion control technique that researchers have been developed. To describe congestion system mechanisms and responses, the system will be simulated by Matlab.

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“…The first of these works can be considered the article [1] published in 1995, although the term "active queue management (AQM) algorithms" was not used in it. Instead the term "congestion control algorithms" was used.…”

Section: Introductionmentioning

confidence: 99%