This document describes the Stream Control Transmission Protocol (SCTP). SCTP is designed to transport PSTN signaling messages over IP networks, but is capable of broader applications.

Stream Control Transmission Protocol

The purpose of this document is to advance NewReno TCP's Fast Retransmit and Fast Recovery algorithms in RFC 2582 from Experimental to Standards Track status.

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The NewReno Modification to TCP's Fast Recovery Algorithm

TCP/IP Illustrated, Volume 1 is a complete and detailed guide to the entire TCP/IP protocol suite - with an important difference from other books on the subject. Rather than just describing what the RFCs say the protocol suite should do, this unique book uses a popular diagnostic tool so you may actually watch the protocols in action.By forcing various conditions to occur - such as connection establishment, timeout and retransmission, and fragmentation - and then displaying the results, TCP/IP Illustrated gives you a much greater understanding of these concepts than words alone could provide. Whether you are new to TCP/IP or you have read other books on the subject, you will come away with an increased understanding of how and why TCP/IP works the way it does, as well as enhanced skill at developing aplications that run over TCP/IP.

TCP/IP Illustrated Vol 1 The Protocols

We describe FAST TCP, a new TCP congestion control algorithm for high-speed long-latency networks, from design to implementation. We highlight the approach taken by FAST TCP to address the four difficulties which the current TCP implementation has at large windows. We describe the architecture and summarize some of the algorithms implemented in our prototype. We characterize its equilibrium and stability properties. We evaluate it experimentally in terms of throughput, fairness, stability, and responsiveness

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FAST TCP: motivation, architecture, algorithms, performance

Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). Despite the abundance of recently proposed schemes, state-of-the-art ABR algorithms suffer from a key limitation: they use fixed control rules based on simplified or inaccurate models of the deployment environment. As a result, existing schemes inevitably fail to achieve optimal performance across a broad set of network conditions and QoE objectives.We propose Pensieve, a system that generates ABR algorithms using reinforcement learning (RL). Pensieve trains a neural network model that selects bitrates for future video chunks based on observations collected by client video players. Pensieve does not rely on pre-programmed models or assumptions about the environment. Instead, it learns to make ABR decisions solely through observations of the resulting performance of past decisions. As a result, Pensieve automatically learns ABR algorithms that adapt to a wide range of environments and QoE metrics. We compare Pensieve to state-of-the-art ABR algorithms using trace-driven and real world experiments spanning a wide variety of network conditions, QoE metrics, and video properties. In all considered scenarios, Pensieve outperforms the best state-of-the-art scheme, with improvements in average QoE of 12%--25%. Pensieve also generalizes well, outperforming existing schemes even on networks for which it was not explicitly trained.

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Neural Adaptive Video Streaming with Pensieve

This document defines TCP's four intertwined congestion control algorithms: slow start, congestion avoidance, fast retransmit, and fast recovery. In addition, the document specifies how TCP should begin transmission after a relatively long idle period, as well as discussing various acknowledgment generation methods.

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W. Stevens

Papers

TCP Congestion Control