The NewReno Modification to TCP's Fast Recovery Algorithm

By Sally Floyd, Thomas R. Henderson, A. Gurtov. Request for Comments, 2004

1,642 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/bc733a199d1b5c399669696c9efb8ee39d9fac37

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. A. Afanasyev
2. Yunhong Gu
3. Sanjay Hegde
4. Cheng Jin
5. David X. Wei
6. K. Claffy
7. Brighten Godfrey
8. R. Venkatesha Prasad

In Control and Systems Engineering

1. Carlo Caini
2. Rosario Firrincieli
3. Aviv Tamar
4. Steven H. Low

Citations in the corpus

• UDT: UDP-based data transfer for high-speed wide area networks (2007, background): “Composable UDT supports a wide variety of control algorithms, including but not limited to, TCP algorithms (e.g., NewReno [24], Vegas [10], FAST [37], Westwood [25], HighSpeed [23], BiC [67], and Scalable [40]), bulk data transfer algorithms (e.g., SABUL [26], RBUDP [33], LambdaStream [66], CHEETAH ”
• FAST TCP: Motivation, Architecture, Algorithms, Performance (2006, methodology): “The congestion control algorithm in the current TCP, which we refer to as Reno in this paper, was developed in 1988 [20] and has gone through several enhancements since, e.g., [1], [ 14 ], [18], [21], [47], [58].”
• TCP Hybla: a TCP enhancement for heterogeneous networks (2004, methodology): “Here we will limit ourselves to recall the main features of the TCP NewReno recovery phase [18], which is basically maintained by TCP Hybla, but the mandatory implementation of a couple of additional features.”
• Bandwidth estimation: metrics, measurement techniques, and tools (2003, background): “Variations in the specificationand implementation of TCP, such as NewReno [3], Reno, or Tahoe, use of SACKs [4] versus cumulative ACKs, selection of the initial window size [5], and several other parameters also affect the TCP throughput.”
• Adaptive forwarding in named data networking (2012, background): “, TCP Vegas [17], TCP CUBIC [32] and TCP NewReno [34].”
• A Deep Reinforcement Learning Perspective on Internet Congestion Control (2019, methodology): “Lastly, the RL scheme is compared only to a variant of TCP from 1999 (namely, TCP NewReno (Floyd & Henderson, 1999)) and to the Q-learning scheme in (Li et al., 2016)).”