Data Center TCP (DCTCP): TCP Congestion Control for Data Centers

Bensley, Stephen; Balasubramanian, Praveen; Judd, Glenn; Eggert, Lars; Thaler, Dave

doi:10.17487/rfc8257

Cited by 39 publications

(16 citation statements)

References 3 publications

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“…Here, the application needs to communicate the metadata and deadline of the data to DTP. In addition to these solutions, there are new congestion control algorithms such as Data Center TCP [19] that uses Explicit Congestion Notification (ECN) [20] to provide window-based control methods. Similarly, IATCP [21] is a rate-based congestion control approach that counts the total number of packets injected to meet the Bandwidth-Delay Product (BDP) of the network dynamically.…”

Section: Motivationmentioning

confidence: 99%

A Method for Enabling Context-Awareness at Transport Layer for Improved Quality-of-Service Control

et al. 2021

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As the 5G systems have introduced network slicing on the virtualized network environment, application-specific QoS (Quality of Service) management has come to the fore to support a range of services, such as eMBB (enhanced Mobile Broadband), URLLC (Ultra-Reliable Low Latency Communications), and mIoT (massive Internet of Things). It is expected for the next-generation systems to support more diverse applications demanding newer and granular quality characteristics for network services. Most of the current transport layer protocols, built on layering design and best-effort paradigm, do not provide the necessary precision and adaptability required for various QoS support. Therefore, we need new protocols that comprehend application behaviours and adapt to dynamic network conditions for more fine-grained QoS enhancement. This paper presents a Context-oriented Transport (CoT) layer for the next-generation network that understands the application context and adapts to varying network conditions with flowbased QoS control. CoT is an end-to-end software solution that improves the underlying network capacity utilization and prioritizes the traffic flows according to quality needs. We prototyped CoT in Linux/Android devices and evaluated the performance with emulated traffic environment. The experiments show that CoT reduces latency by up to 16.5% and improves average throughput by up to 30%, compared with Android 10 in LTE network, resulting from enhanced traffic classification and network utilization.

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Section: Motivationmentioning

confidence: 99%

A Method for Enabling Context-Awareness at Transport Layer for Improved Quality-of-Service Control

et al. 2021

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“…The L4S Architecture is a solution that enables low latency, low loss and a scalable throughput in novel applications coexisting on shared network bottlenecks. It aims to break network ossification and calls for evolution, making it possible to run scalable transport protocols such as the DCTCP [129] and MDTCP [130] over the same access networks as those of non-scalable transport protocols such as TCP CUBIC/Reno.…”

Section: Pr-mptcp+ [106]mentioning

confidence: 99%

A Survey of End-to-End Solutions for Reliable Low-Latency Communications in 5G Networks

Rico

Merino

2020

IEEE Access

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Very low latency and high reliability are two of the main requirements for new applications exploiting 5G networks. This is the case for the remote operation of robots or vehicles, the autonomous interaction of equipment in a factory, autonomous driving and tactile internet applications. Although the TCP/IP stack has been sufficient as the end-to-end solution for most of the history of the Internet, a number of surveys have appeared recently presenting many different methods for managing the end-to-end communication to meet the requirements of various technologies such as that of 5G networks. In this paper, we present a novel classification of the literature focused on new end-to-end solutions and the creation of services towards the support of low latency (1 ms) and high reliability (10-9 error rate) in current and future 5G networks. We specifically highlight how the proposals can be classified according to enabling technologies and the specific method used to achieve success in terms of the latency and reliability. The literature related to end-to-end solutions for reliable low-latency communications are organized according to three main topics: (i) end-to-end protocols that improve communication in terms of latency and reliability, (ii) functionality or technologies implemented on the network to support the current demands, and (iii) application programming interfaces that enhance the correct utilization of those protocols and additional technologies.

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“…The random early detection (RED) can also be considered as in this line [23]. A method has recently been proposed that reports not only the existence of congestion but also an increase in queuing delay for data-center networks [24].…”

Section: Transport-and Lower-layer Congestion Controlsmentioning

confidence: 99%

Modeling Upper Layer Reaction to QoS Degradation as a Congestion Avoidance Mechanism

Harada

Ishibashi

Kawahara

2020

IEICE Trans. Commun.

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On the Internet, end hosts and network nodes interdependently work to smoothly transfer traffic. Observed traffic dynamics are the result of those interactions among those entities. To manage Internet traffic to provide satisfactory quality services, such dynamics need to be well understood to predict traffic patterns. In particular, some nodes have a function that sends back-pressure signals to backward nodes to reduce their sending rate and mitigate congestion. Transmission Control Protocol (TCP) congestion control in end-hosts also mitigates traffic deviation to eliminate temporary congestion by reducing the TCP sending rate. How these congestion controls mitigate congestion has been extensively investigated. However, these controls only throttle their sending rate but do not reduce traffic volume. Such congestion control fails if congestion is persistent, e.g., for hours, because unsent traffic demand will infinitely accumulate. However, on the actual Internet, even with persistent congestion, such accumulation does not seem to occur. During congestion, users and/or applications tend to reduce their traffic demand in reaction to quality of service (QoS) degradation to avoid negative service experience. We previously estimated that 2% packet loss results in 23% traffic reduction because of this upper-layer reaction [1]. We view this reduction as an upper-layer congestion-avoidance mechanism and construct a closed-loop model of this mechanism, which we call the Upper-Layer Closed-Loop (ULCL) model. We also show that by using ULCL, we can predict the degree of QoS degradation and traffic reduction as an equilibrium of the feedback loop. We applied our model to traffic and packet-loss ratio time series data gathered in an actual network and demonstrate that it effectively estimates actual traffic and packet-loss ratio.

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Data Center TCP (DCTCP): TCP Congestion Control for Data Centers

Cited by 39 publications

References 3 publications

A Method for Enabling Context-Awareness at Transport Layer for Improved Quality-of-Service Control

A Method for Enabling Context-Awareness at Transport Layer for Improved Quality-of-Service Control

A Survey of End-to-End Solutions for Reliable Low-Latency Communications in 5G Networks

Modeling Upper Layer Reaction to QoS Degradation as a Congestion Avoidance Mechanism

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