A bidirectional congestion control transport protocol for the internet of drones

The Transport-Level Requirements of the Internet-Based Streaming

Encyclopedia of Information Communication Technology ◽

10.4018/978-1-59904-845-1.ch102 ◽

2009 ◽

pp. 775-781

Author(s):

Dávid Tegze ◽

Gábor Hosszú

Keyword(s):

Congestion Control ◽

Traffic Analysis ◽

The Internet ◽

Transport Protocol ◽

Control Schemes ◽

One To One ◽

Uniform Manner

This article presents the comparison of different transport level congestion control schemes, including variants of the TCP (Postel, 1981). The protocol mechanisms, implemented in various protocols are hard to investigate in a uniform manner (Hosszú, 2005); therefore, the simulator SimCast (Simulator for multicast) is developed for traffic analysis of the unicast (one-to-one communication) and multicast (one-to-many communication) streams. In this article, the TCP and other transport protocol mechanisms will be compared using the SimCast simulator (Orosz & Tegze, 2001). The simulated results are presented through examples.

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TRCCTP: A traffic redirection based congestion control transport protocol for wireless sensor networks

2015 IEEE SENSORS ◽

10.1109/icsens.2015.7370452 ◽

2015 ◽

Cited By ~ 2

Author(s):

Trilok Chand ◽

Bhisham Sharma ◽

Manpreet Kour

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Congestion Control ◽

Wireless Sensor ◽

Transport Protocol

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A Model-based Scalable Reliable Multicast Transport Protocol for Satellite Networks

Journal of Communications Software and Systems ◽

10.24138/jcomss.v1i1.313 ◽

2017 ◽

Vol 1 (1) ◽

pp. 24

Author(s):

Prawit Chumchu ◽

Roksana Boreli ◽

Aruna Seneviratne

Keyword(s):

Congestion Control ◽

Error Control ◽

Forward Error Correction ◽

Transmission Control Protocol ◽

Window Size ◽

Satellite Networks ◽

Transport Protocol ◽

Reliable Multicast ◽

Auto Repeat Request ◽

Control Part

In this paper, we design a new scalable reliable multicast transport protocol for satellite networks (RMT). This paper is the extensions of paper in [18]. The proposed protocoldoes not require inspection and/or interception of packets at intermediate nodes. The protocol would not require anymodification of satellites, which could be bent-pipe satellites or onboard processing satellites. The proposed protocol is divided in 2 parts: error control part and congestion control part. In error control part, we intend to solve feedback implosion and improve scalability by using a new hybrid of ARQ (Auto Repeat Request) and adaptive forward error correction (AFEC). The AFEC algorithm adapts proactive redundancy levels following the number of receivers and average packet loss rate. This leads to a number of transmissions and the number of feedback signals are virtually independent of the number of receivers. Therefore, wireless link utilization used by the proposed protocol is virtually independent of the number of multicast receivers. In congestion control part, the proposed protocol employs a new window-based congestion control scheme, which is optimized for satellite networks. To be fair to the other traffics, the congestion control mimics congestion control in the wellknown Transmission Control Protocol (TCP) which relies on “packet conservation” principle. To reduce feedback implosion, only a few receivers, ACKers, are selected to report the receiving status. In addition, in order to avoid “drop-to-zero” problem, we use a new simple wireless loss filter algorithm. This loss filter algorithm significantly reduces the probability of the congestion window size to be unnecessarily reduced because of common wireless losses. Furthermore, to improve achievable throughput, we employ slow start threshold adaptation based on estimated bandwidth. The congestion control also deals with variations in network conditions by dynamically electing ACKers.

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Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?

Applied Sciences ◽

10.3390/app10186164 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6164

Author(s):

Luis Diez ◽

Alfonso Fernández ◽

Muhammad Khan ◽

Yasir Zaki ◽

Ramón Agüero

Keyword(s):

Machine Learning ◽

Congestion Control ◽

Arrival Time ◽

Wireless Channel ◽

Transport Layer ◽

Transport Protocol ◽

Length Variation ◽

State Delay ◽

Extensive Analysis ◽

Buffer Length

It is well known that transport protocol performance is severely hindered by wireless channel impairments. We study the applicability of Machine Learning (ML) techniques to predict congestion status of 5G access networks, in particular mmWave links. We use realistic traces, using the 3GPP channel models, without being affected using legacy congestion-control solutions. We start by identifying the metrics that might be exploited from the transport layer to learn the congestion state: delay and inter-arrival time. We formally study their correlation with the perceived congestion, which we ascertain based on buffer length variation. Then, we conduct an extensive analysis of various unsupervised and supervised solutions, which are used as a benchmark. The results yield that unsupervised ML solutions can detect a large percentage of congestion situations and they could thus bring interesting possibilities when designing congestion-control solutions for next-generation transport protocols.

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TCP for High-Speed Networks

Encyclopedia of Internet Technologies and Applications ◽

10.4018/978-1-59140-993-9.ch088 ◽

2008 ◽

pp. 626-632

Author(s):

Nelson Luís Saldanha da Fonseca ◽

Neila Fernanda Michel

Keyword(s):

Congestion Control ◽

High Speed ◽

Transmission Control Protocol ◽

Window Size ◽

Transport Layer ◽

The Internet ◽

Congestion Window ◽

High Speed Networks ◽

Congestion Control Mechanism ◽

Tcp Reno

In response to a series of collapses due to congestion on the Internet in the mid-’80s, congestion control was added to the transmission control protocol (TCP) (Jacobson, 1988), thus allowing individual connections to control the amount of traffic they inject into the network. This control involves regulating the size of the congestion window (cwnd) to impose a limit on the size of the transmission window. In the most deployed TCP variant on the Internet, TCP Reno (Allman, Floyd, & Partridge, 2002), changes in congestion window size are driven by the loss of segments. Congestion window size is increased by 1/cwnd for each acknowledgement (ack) received, and reduced to half for the loss of a segment in a pattern known as additive increase multiplicative decrease (AIMD). Although this congestion control mechanism was derived at a time when the line speed was of the order of 56 kbs, it has performed remarkably well given that the speed, size, load, and connectivity of the Internet have increased by approximately six orders of magnitude in the past 15 years. However, the AIMD pattern of window growth seriously limits efficienct operation of TCP-Reno over high-capacity links, so that the transport layer is the network bottleneck. This text explains the major challenges involved in using TCP for high-speed networks and briefly describes some of the variations of TCP designed to overcome these challenges.

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Joint Design of Source Rate Control and QoS-Aware Congestion Control for Video Streaming Over the Internet

IEEE Transactions on Multimedia ◽

10.1109/tmm.2006.886284 ◽

2007 ◽

Vol 9 (2) ◽

pp. 366-376 ◽

Cited By ~ 30

Author(s):

Peng Zhu ◽

W. Zeng ◽

Chunwen Li

Keyword(s):

Congestion Control ◽

Video Streaming ◽

Rate Control ◽

The Internet ◽

Joint Design ◽

Source Rate ◽

Source Rate Control

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Stability of the Internet congestion control with diverse delays

Automatica ◽

10.1016/j.automatica.2004.03.015 ◽

2004 ◽

Vol 40 (9) ◽

pp. 1533-1541 ◽

Cited By ~ 45

Author(s):

Yu-Ping Tian ◽

Hong-Yong Yang

Keyword(s):

Congestion Control ◽

The Internet ◽

Internet Congestion Control ◽

Internet Congestion

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Buffer Occupancy-Based Transport to Reduce Flow Completion Time of Short Flows in Data Center Networks

Symmetry ◽

10.3390/sym11050646 ◽

2019 ◽

Vol 11 (5) ◽

pp. 646 ◽

Cited By ~ 3

Author(s):

Hasnain Ahmed ◽

Muhammad Junaid Arshad

Keyword(s):

Congestion Control ◽

Data Center ◽

Completion Time ◽

Current Data ◽

Transport Protocol ◽

Transport Protocols ◽

Data Center Networks ◽

Buffer Occupancy ◽

Control Scheme ◽

Precise Measure

Today’s data centers host a variety of different applications that impose specific requirements for their flows. Applications that generate short flows are usually latency sensitive; they require their flows to be completed as fast as possible. Short flows suffer to quickly increase their sending rate due to the existing long flows occupying most of the available capacity. This problem is caused due to the slow convergence of the current data center transport protocols. In this paper, we present a buffer occupancy-based transport protocol (BOTCP) to reduce flow completion time of short flows. BOTCP consists of two parts: (i) A novel buffer occupancy-based congestion signal, and (ii) a congestion control scheme that uses the congestion signal to reduce flow completion time of short flows. The proposed buffer occupancy-based congestion signal gives a precise measure of congestion. The congestion control scheme makes a differentiated treatment of short and long flows to reduce flow completion time of short flows. The results show that BOTCP significantly improves flow completion time of short flows over the existing transport protocols in data center networks.

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