Artificial Intelligence (AI) based ECN adaptive reconfiguration for datacenter networks
draft-zhuang-tsvwg-ai-ecn-for-dcn-00

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TSVWG                                                          Y. Zhuang
Internet-Draft                                                  B. Zhang
Intended status: Informational                                    H. Pan
Expires: April 20, 2020                    Huawei Technologies Co., Ltd.
                                                        October 18, 2019

  Artificial Intelligence (AI) based ECN adaptive reconfiguration for
                          datacenter networks
                  draft-zhuang-tsvwg-ai-ecn-for-dcn-00

Abstract

   This document is to provide an artificial intelligence (AI) based ECN
   adaptive reconfiguration for datacenter networks.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on April 20, 2020.

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   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Background  . . . . . . . . . . . . . . . . . . . . . . .   2
     1.2.  Intent  . . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.3.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Architecture of the AI ECN datacenter networks  . . . . . . .   3
   3.  Scene-based ECN adaptive reconfiguration with AI  . . . . . .   4
     3.1.  Scene Training  . . . . . . . . . . . . . . . . . . . . .   5
     3.2.  Scene Identification and ECN Adaptive Reconfiguration . .   5
   4.  Data collection and AI ECN adaptive reconfiguration . . . . .   5
     4.1.  Data collection . . . . . . . . . . . . . . . . . . . . .   5
     4.2.  ECN adaptive Reconfiguration  . . . . . . . . . . . . . .   6
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   6.  Manageability Consideration . . . . . . . . . . . . . . . . .   6
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   6
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .   6
     8.2.  Informative References  . . . . . . . . . . . . . . . . .   6
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   7

1.  Introduction

1.1.  Background

   As defined in [RFC3168], Explicit Congestion Notification is
   introduced for IP to allow congestion to be signaled before dropping
   packets.  As such, the latency of applications is reduced due to less
   retransmission of the dropped packets.  Besides, MPLS also supports
   ECN defined in [RFC6679].  For tunneling, [RFC6040] defines how ECN
   should be constructed in the case of IP-in-IP tunnels.

   Meanwhile, the upper layer transports protocols, like TCP in
   [RFC3168] and UDP based protocols DCCP in [RFC4341][RFC4342][RFC5632]
   and RTP in [RFC6679] are defined to support ECN-capable functions.

   With ECN marking, active queue management (AQM) can choose a non-
   packet loss way to indicate congestion on the device, rather than
   dropping packets which might ask for packet retransmission and
   increase the latency.  By using AQM in network devices, it can signal
   to common congestion-controlled transports to manage the queue length
   in the buffer and reduce the latency of traffics.  Random Early
   Detection (RED) specified in [RFC2309]is one of the AQM algorithms
   that recommended to be implemented in routers.

   As stated in [RFC7567], with proper parameters, RED can be an
   effective algorithm.  However, dynamically predicting the set of

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