High Speed Networks Need Proactive Congestion Control
Author(s) -
Lavanya Jose,
Lisa Yan,
Mohammad Alizadeh,
George Varghese,
Nick McKeown,
Sachin Katti
Publication year - 2015
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2834050.2834096
Subject(s) - computer science , network congestion , convergence (economics) , decoding methods , flow control (data) , gradient descent , algorithm , rate of convergence , computer network , real time computing , artificial intelligence , network packet , artificial neural network , economics , economic growth , channel (broadcasting)
As datacenter speeds scale to 100 Gb/s and beyond, traditional congestion control algorithms like TCP and RCP converge slowly to steady sending rates, which leads to poorer and less predictable user performance. These reactive algorithms use congestion signals to perform gradient descent to approach ideal sending rates, causing poor convergence times. In this paper, we propose a proactive congestion control algorithm called PERC, which explicitly computes rates independently of congestion signals in a decentralized fashion. Inspired by message-passing algorithms with traction in other fields (e.g., modern Low Density Parity Check decoding algorithms), PERC improves convergence times by a factor of 7 compared to reactive explicit rate control protocols such as RCP. This fast convergence reduces tail flow completion time (FCT) significantly in high speed networks; for example, simulations of a realistic workloads in a 100 Gb/s network show that PERC achieves up to 4x lower 99th percentile FCT compared to RCP.
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