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HPCC++: Enhanced High Precision Congestion Control

Document Type Active Internet-Draft (individual)
Authors Rui Miao , Hongqiang H. Liu , Rong Pan , Jeongkeun Lee , Changhoon Kim , Barak Gafni , Yuval Shpigelman , Jeff Tantsura
Last updated 2021-12-07
Replaces draft-pan-tsvwg-hpccplus
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Network Working Group                                            R. Miao
Internet-Draft                                                    H. Liu
Intended status: Experimental                              Alibaba Group
Expires: 10 June 2022                                             R. Pan
                                                                  J. Lee
                                                                  C. Kim
                                                       Intel Corporation
                                                                B. Gafni
                                                           Y. Shpigelman
                                             Mellanox Technologies, Inc.
                                                             J. Tantsura
                                                   Microsoft Corporation
                                                         7 December 2021

           HPCC++: Enhanced High Precision Congestion Control


   Congestion control (CC) is the key to achieving ultra-low latency,
   high bandwidth and network stability in high-speed networks.
   However, the existing high-speed CC schemes have inherent limitations
   for reaching these goals.

   In this document, we describe HPCC++ (High Precision Congestion
   Control), a new high-speed CC mechanism which achieves the three
   goals simultaneously.  HPCC++ leverages inband telemetry to obtain
   precise link load information and controls traffic precisely.  By
   addressing challenges such as delayed signaling during congestion and
   overreaction to the congestion signaling using inband and granular
   telemetry, HPCC++ can quickly converge to utilize all the available
   bandwidth while avoiding congestion, and can maintain near-zero in-
   network queues for ultra-low latency.  HPCC++ is also fair and easy
   to deploy in hardware, implementable with commodity NICs and

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
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at

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   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
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   This Internet-Draft will expire on 10 June 2022.

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   Copyright (c) 2021 IETF Trust and the persons identified as the
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   This document is subject to BCP 78 and the IETF Trust's Legal
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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  System Overview . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  HPCC++ Algorithm  . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Notations . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.2.  Design Functions and Procedures . . . . . . . . . . . . .   6
   5.  Configuration Parameters  . . . . . . . . . . . . . . . . . .   8
   6.  Design Enhancement and Implementation . . . . . . . . . . . .   8
     6.1.  HPCC++ Guidelines . . . . . . . . . . . . . . . . . . . .   9
     6.2.  Receiver-based HPCC . . . . . . . . . . . . . . . . . . .   9
   7.  Reference Implementations . . . . . . . . . . . . . . . . . .  10
     7.1.  Inband telemetry padding at the network elements  . . . .  10
     7.2.  Congestion control at NICs  . . . . . . . . . . . . . . .  10
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   9.  Discussion  . . . . . . . . . . . . . . . . . . . . . . . . .  12
     9.1.  Internet Deployment . . . . . . . . . . . . . . . . . . .  12
     9.2.  Switch-assisted congestion control  . . . . . . . . . . .  12
     9.3.  Work with transport protocols . . . . . . . . . . . . . .  13
     9.4.  Work with QoS queuing . . . . . . . . . . . . . . . . . .  13
   10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  14
   11. Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  14
   12. References  . . . . . . . . . . . . . . . . . . . . . . . . .  14
     12.1.  Normative References . . . . . . . . . . . . . . . . . .  14
     12.2.  Informative References . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

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1.  Introduction

   The link speed in data center networks has grown from 1Gbps to
   100Gbps in the past decade, and this growth is continuing.  Ultralow
   latency and high bandwidth, which are demanded by more and more
   applications, are two critical requirements in today's and future
   high-speed networks.

   Given that traditional software-based network stacks in hosts can no
   longer sustain the critical latency and bandwidth requirements as
   described in [Zhu-SIGCOMM2015], offloading network stacks into
   hardware is an inevitable direction in high-speed networks.  As an
   example, large-scale networks with RDMA (remote direct memory access)
   often uses hardware-offloading solutions.  In some cases, the RDMA
   networks still face fundamental challenges to reconcile low latency,
   high bandwidth utilization, and high stability.

   This document describes a new congestion control mechanism, HPCC++
   (Enhanced High Precision Congestion Control), for large-scale, high-
   speed networks.  The key idea behind HPCC++ is to leverage the
   precise link load information from signaled through inband telemetry
   to compute accurate flow rate updates.  Unlike existing approaches
   that often require a large number of iterations to find the proper
   flow rates, HPCC++ requires only one rate update step in most cases.
   Using precise information from inband telemetry enables HPCC++ to
   address the limitations in current congestion control schemes.
   First, HPCC++ senders can quickly ramp up flow rates for high
   utilization and ramp down flow rates for congestion avoidance.
   Second, HPCC++ senders can quickly adjust the flow rates to keep each
   link's output rate slightly lower than the link's capacity,
   preventing queues from being built-up as well as preserving high link
   utilization.  Finally, since sending rates are computed precisely
   based on direct measurements at switches, HPCC++ requires merely
   three independent parameters that are used to tune fairness and

   The base form of HPCC++ is the original HPCC algorithm and its full
   description can be found in [SIGCOMM-HPCC].  While the original
   design lays the foundation for inband telemetry based precision
   congestion control, HPCC++ is an enhanced version which takes into
   account system constraints and aims to reduce the design overhead and
   further improves the performance.  Section 6 describes these detailed
   proposed design enhancements and guidelines.

   This document describes the architecture changes in switches and end-
   hosts to support the needed tranmission of inband telemetry and its
   consumption, that imporves the efficiency in handling network

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2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

3.  System Overview

   Figure 1 shows the end-to-end system that HPCC++ operates in.  During
   the traverse of the packet from the sender to the receiver, each
   switch along the path inserts inband telemetry that reports the
   current state of the packet's egress port, including timestamp (ts),
   queue length (qLen), transmitted bytes (txBytes), and the link
   bandwidth capacity (B), together with switch_ID and port_ID.  When
   the receiver gets the packet, it may copy all the inband telemetry
   recorded from the network to the ACK message it sends back to the
   sender, and then the sender decides how to adjust its flow rate each
   time it receives an ACK with network load information.
   Alternatively, the receiver may calculate the flow rate based on the
   inband telemetry information and feedback the calculated rate back to
   the sender.  The notification packets would include delayed ack
   information as well.

   Note that there also exist network nodes along the reverse
   (potentially uncongested) path that the RTCP feedback reports
   traverse.  Those network nodes are not shown in the figure for sake
   of brevity.

    +---------+  pkt    +-------+ pkt+tlm +-------+ pkt+tlm +----------+
    |  Data   |-------->|       |-------->|       |-------->| Data     |
    |  Sender |=========|Switch1|=========|Switch2|=========| Receiver |
    +---------+ Link-0  +-------+  Link-1 +-------+  Link-2 +----------+
        /|\                                                        |
         |                                                         |
                         Notification Packets/ACKs

             Figure 1: System Overview (tlm=inband telemtry)

   *  Data sender: responsible for controlling inflight bytes.  HPCC++
      is a window-based congestion control scheme that controls the
      number of inflight bytes.  The inflight bytes mean the amount of
      data that have been sent, but not acknowledged by the sender yet.
      Controlling inflight bytes has an important advantage compared to
      controlling rates.  In the absence of congestion, the inflight

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      bytes and rate are interchangeable with equation inflight = rate *
      T where T is the base propagation RTT.  The rate can be calculated
      locally or obtained from the notification packet.  The sender may
      further use the data pacing mechanism, potentially implemented in
      hardware, to limit the rate accordingly.

   *  Network nodes: responsible of inserting the inband telemetry
      information to the data packet.  The inband telemetry information
      reports the current load of the packet's egress port, including
      timestamp (ts), queue length (qLen), transmitted bytes (txBytes),
      and link bandwidth capacity (B).  Besides, the inband telemetry
      contains switch_ID and port_ID to identify a link.

   *  Data receiver: responsible for either reflecting back the inband
      telemetry information in the data packet or calculating the proper
      flow rate based on network congestion information in inband
      telemetry and sending notification packets back to the sender.

4.  HPCC++ Algorithm

   HPCC++ is a window-based congestion control algorithm.  The key
   design choice of HPCC++ is to rely on network nodes to provide fine-
   grained load information, such as queue size and accumulated tx/rx
   traffic to compute precise flow rates.  This has two major benefits:
   (i) HPCC++ can quickly converge to proper flow rates to highly
   utilize bandwidth while avoiding congestion; and (ii) HPCC++ can
   consistently maintain a close-to-zero queue for low latency.

   This section introduces the list of notations and describes the core
   congestion control algorithm.

4.1.  Notations

   This section summarizes the list of variables and parameters used in
   the HPCC++ algorithm.  Figure 3 also includes the default values for
   choosing the algorithm parameters either to represent a typical
   setting in practical applications or based on theoretical and
   simulation studies.

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     | Notation     | Variable Name                                   |
     | W_i          | Window for flow i                               |
     | Wc_i         | Reference window for flow i                     |
     | B_j          | Bandwidth for Link j                            |
     | I_j          | Estimated inflight bytes for Link j             |
     | U_j          | Normalized inflight bytes for Link j            |
     | qlen         | Telemetry info: link j queue length             |
     | txRate       | Telemetry info: link j output rate              |
     | ts           | Telemetry info: timestamp                       |
     | txBytes      | Telemetry info: link j total transmitted bytes  |
     |              |                  associated with timestamp ts   |

                        Figure 2: List of variables.

    | Notation     | Parameter Name                   | Default Value  |
    | T            | Known baseline RTT               |    5us         |
    | eta          | Target link utilization          |    95%         |
    | maxStage     | Maximum stages for additive      |                |
    |              | increases                        |    5           |
    | N            | Maximum number of flows          |    ...         |
    | W_ai         | Additive increase amount         |    ...         |

    Figure 3: List of algorithm parameters and their default values.

4.2.  Design Functions and Procedures

   The HPCC++ algorithm can be outlined as below:

   1: Function MeasureInflight(ack)
   2:    u = 0;
   3:    for each link i on the path do
   4:                  ack.L[i].txBytes-L[i].txBytes
             txRate =  ----------------------------- ;
   5:               min(ack.L[i].qlen,L[i].qlen)      txRate
              u' = ----------------------------- +  ---------- ;
                        ack.L[i].B*T                ack.L[i].B
   6:         if u' > u then
   7:             u = u'; tau = ack.L[i].ts -  L[i].ts;
   8:     tau = min(tau, T);
   9:     U = (1 - tau/T)*U + tau/T*u;
   10:    return U;

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   11: Function ComputeWind(U, updateWc)
   12:    if U >= eta or incStage >= maxStagee then
   13:             Wc
              W = ----- + W_ai;
   14:        if updateWc then
   15:            incStagee = 0; Wc = W ;
   16:    else
   17:        W = Wc + W_ai ;
   18:        if updateWc then
   19:            incStage++; Wc = W ;
   20:    return W

   21: Procedure NewAck(ack)
   22:    if ack.seq > lastUpdateSeq then
   23:        W = ComputeWind(MeasureInflight(ack), True);
   24:        lastUpdateSeq = snd_nxt;
   25:    else
   26:        W = ComputeWind(MeasureInflight(ack), False);
   27:    R = W/T; L = ack.L;

   The above illustrates the overall process of CC at the sender side
   for a single flow.  Each newly received ACK message triggers the
   procedure NewACK at Line 21.  At Line 22, the variable lastUpdateSeq
   is used to remember the first packet sent with a new W c , and the
   sequence number in the incoming ACK should be larger than
   lastUpdateSeq to trigger a new sync betweenW c andW (Line 14-15 and
   18-19).  The sender also remembers the pacing rate and current inband
   telemetry information at Line 27.  The sender computes a new window
   size W at Line 23 or Line 26, depending on whether to update W c ,
   with function MeasureInflight and ComputeWind.  Function
   MeasureInflight estimates normalized inflight bytes with Eqn (2) at
   Line 5.  First, it computes txRate of each link from the current and
   last accumulated transferred bytes txBytes and timestamp ts (Line 4).
   It also uses the minimum of the current and last qlen to filter out
   noises in qlen (Line 5).  The loop from Line 3 to 7 selects maxi(Ui)
   in Eqn. (3).  Instead of directly using maxi(Ui), we use an EWMA
   (Exponentially Weighted Moving Average) to filter the noises from
   timer inaccuracy and transient queues.  (Line 9).  Function
   ComputeWind combines multiplicative increase/ decrease (MI/MD) and
   additive increase (AI) to balance the reaction speed and fairness.
   If a sender finds it should increase the window size, it first tries
   AI for maxStage times with the stepWAI (Line 17).  If it still finds
   room to increase after maxStage times of AI or the normalized
   inflight bytes is above, it calls Eqn (4) once to quickly ramp up or
   ramp down the window size (Line 12-13).

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5.  Configuration Parameters

   HPCC++ has three easy-to-set parameters: eta, maxStagee, and W_ai.
   eta controls a simple tradeoff between utilization and transient
   queue length (due to the temporary collision of packets caused by
   their random arrivals, so we set it to 95% by default, which only
   loses 5% bandwidth but achieves almost zero queue. maxStage controls
   a simple tradeoff between steady state stability and the speed to
   reclaim free bandwidth.  We find maxStage = 5 is conservatively large
   for stability, while the speed of reclaiming free bandwidth is still
   much faster than traditional additive increase, especially in high
   bandwidth networks.  W_ai controls the tradeoff between the maximum
   number of concurrent flows on a link that can sustain near-zero
   queues and the speed of convergence to fairness.  Note that none of
   the three parameters are reliability-critical.

   HPCC++'s design brings advantages to short-lived flows, by allowing
   flows starting at line-rate and the separation of utilization
   convergence and fairness convergence.  HPCC++ achieves fast
   utilization convergence to mitigate congestion in almost one round-
   trip time, while allows flows to gradually converge to fairness.
   This design feature of HPCC++ is especially helpful for the workload
   of datacenter applications, where flows are usually short and
   latency-sensitive.  Normally we set a very small W_ai to support a
   large number of concurrent flows on a link, because slower fairness
   is not critical.  A rule of thumb is to set W_ai = W_init*(1-eta) / N
   where N is the expected or receiver reported maximum number of
   concurrent flows on a link.  The intuition is that the total additive
   increase every round (N*W_ai ) should not exceed the bandwidth
   headroom, and thus no queue forms.  Even if the actual number of
   concurrent flows on a link exceeds N, the CC is still stable and
   achieves full utilization, but just cannot maintain zero queues.

6.  Design Enhancement and Implementation

   The basic design of HPCC++, i.e. HPCC, as described above is to add
   inband telemetry information into every data packet to react to
   congestion as soon as the very first packet observing the network
   congestion.  This is especially helpful to reduce the risk of severe
   congestion in incast scenarios at the first round-trip time.  In
   addition, original HPCC's algorithm introduction of Wc is for the
   purpose of solving the over-reaction issue from using this per-packet

   Alternatively, the inband telemetry information needs not to be added
   to every data packet to reduce the overhead.  Switches can attach
   inband telemetry less frequently, e.g., once per RTT or upon
   congestion occurance.

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6.1.  HPCC++ Guidelines

   To ensure network stability, HPCC++ establishes a few guidelines for
   different implementations:

   *  The algorithm should commit the window/rate update at most once
      per round-trip time, similar to the procedure of updating Wc.

   *  To support different workloads and to properly set W_ai, HPCC++
      allows the option to incorporate mechanisms to speed up the
      fairness convergence.

   *  The switch should capture inband telemetry information that
      includes link load (txBytes, qlen, ts) and link spec (switch_ID,
      port_ID, B) at the egress port.  Note, each switch should record
      all those information at the single snapshot to achieve a precise
      link load estimate.

   *  HPCC++ can use a probe packet to query the inband telemetry
      information.  Thereby, the probe packets should take the same
      routing path and QoS queueing with the data packets.

   As long the above guidelines are met, this document does not mandate
   a particular inband telemetry header format or encapsulation, which
   are orthogonal to the HPCC++ algorithm described in this document.
   The algorithm can be implemented with a choice of inband telemetry
   protocols, such as in-band network telemetry [P4-INT], IOAM
   [I-D.ietf-ippm-ioam-data], IFA [I-D.ietf-kumar-ippm-ifa] and others.
   In fact, the emerging inband telemetry protocols can inform the
   evolution for a broader range of protocols and network functions,
   where this document leverages the trend to propose the architecture
   change to support HPCC++ algorithm.

6.2.  Receiver-based HPCC

   Note that the window/rate calculation can be implemented at either
   the data sender or the data receiver.  If the ACK packets already
   exist for reliability purpose, the inband telemetry information can
   be echoed back to the sender via ACK self-clocking.  Not all ACK
   packets need to carry the inband telemetry information.  To reduce
   the Packet Per Second (PPS) overhead, the receiver may examine the
   inband telemetry information and adopt the technique of delayed ACKs
   that only sends out an ACK for a few of received packets.  In order
   to reduce PPS even further, one may implement the algorithm at the
   receiver and feedback the calculated window in the ACK packet once
   every RTT.

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   The receiver-based algorithm, Rx-HPCC, is based on int.L, which is
   the inband telemetry information in the packet header.  The receiver
   performs the same functions except using int.L instead of ack.L.  The
   new function NewINT(int.L) is to replace NewACK(int.L)

   28:   Procedure NewINT(int.L)
   29:   if now > (lastUpdateTime + T) then
   30:      W = ComputeWind(MeasureInflight(int), True);
   31:      send_ack(W)
   32:      lastUpdateTime = now;
   33:   else
   34:      W = ComputeWind(MeasureInflight(int), False);

   Here, since the receiver does not know the starting sequence number
   of a burst, it simply records the lastUpdateTime.  If time T has
   passed since lastUpdateTime, the algorithm would recalcuate Wc as in
   Line 30 and send out the ACK packet which would include W
   information.  Otherwise, it would just update W information locally.
   This would reduce the amount of traffic that needs to be feedback to
   the data sender.

   Note that the receiver can also measure the number of outstanding
   flows, N, if the last hop is the congestion point and use this
   information to dynamically adjust W_ai to achieve better fairness.
   The improvement would allow flows to quickly converge to fairness
   without causing large swings under heavy load.

7.  Reference Implementations

   A prototype of HPCC++ is implemented in NICs to realize the
   congestion control algorithm and in switches to realize the inband
   telemetry feature.

7.1.  Inband telemetry padding at the network elements

   HPCC++ only relies on packets to share information across senders,
   receivers, and switches.  HPCC++ is open to a variety of inband
   telemetry format standards.  Inside a data center, the path length is
   often no more than 5 hops.  The overhead of the inband telemetry
   padding for HPCC++ is considered to be low.

7.2.  Congestion control at NICs

   Figure 4 shows HPCC++ implementation on a NIC.  The NIC provides an
   HPCC++ module that resides on the data path of the NIC, HPCC++
   modules realize both sender and receiver roles.

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  |  +---------+ window update +-----------+ PktSend +-----------+   |
  |  |         |-------------->| Scheduler |-------> |Tx pipeline|---+->
  |  |         | rate update   +-----------+         +-----------+   |
  |  |  HPCC++ |                                           ^         |
  |  |         |                           inband telemetry|         |
  |  |  module |                                           |         |
  |  |         |                                     +-----+-----+   |
  |  |         |<----------------------------------- |Rx pipeline| <-+--
  |  +---------+      telemetry response event       +-----------+   |

               Figure 4: Overview of NIC Implementation

   1.  Sender side flow

   The HPCC++ module running the HPCC CC algorithm in the sender side
   for every flow in the NIC.  Flow can be defined by some transport
   parameters including 5-tuples, destination QP (queue pair), etc.  It
   receives inband telemetry response events per flow which are
   generated from the RX pipeline, adjusts the sending window and rate,
   and update the scheduler on the rate and window of the flow.

   The scheduler contains a pacing mechanism that determine the flow
   rate by the value it got from the algorithm.  It also maintains the
   current sending window size for active flows.  If the pacing
   mechanism and the flow's sending window permits, the scheduler
   invokes for the flow a PktSend command to TX pipeline.

   The TX pipeline implements packet processing.  Once it receives the
   PktSend event with flow ID from the scheduler, it generates the
   corresponding packet and delivers to the Network.  If a sent packet
   should collect telemetry on its way the TX pipeline may add
   indications/headers that triggers the network elements to add
   telemetry data according to the inband telemetry protocol in use.
   The telemetry can be collected by the data packet or by dedicated
   prob packets generated in the TX pipeline.

   The RX pipe parses the incoming packets from the network and
   identifies whether telemetry is embedded in the parsed packet.  On
   receiving a telemetry response packet, the RX pipeline extracts the
   network status from the packet and passes it to the HPCC++ module for
   processing.  A telemetry response packet can be an ACK containing
   inband telemetry, or a dedicated telemetry response prob packet.

   2.  Receiver side flow

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   On receiving a packet containing inband telemetry, the RX pipeline
   extracts the network status, and the flow parameters from the packet
   and passes it to the TX pipeline.  The packet can be a data packet
   containing inband telemetry, or a dedicated telemetry request prob
   packet.  The Tx pipeline may process and edit the telemetry data, and
   then sends back to the sender the data using either an ACK packet of
   the flow or a dedicated telemetry response packet.

8.  IANA Considerations

   This document makes no request of IANA.

9.  Discussion

9.1.  Internet Deployment

   Although the discussion above mainly focuses on the data center
   environment, HPCC++ can be adopted at Internet at large.  There are
   several security considerations one should be aware of.

   There may rise privacy concern when the telemetry information is
   conveyed across Autonomous Systems (ASes) and back to end-users.  The
   link load information captured in telemetry can potentially reveal
   the provider's network capacity, route utilization, scheduling
   policy, etc.  Those usually are considered to be sensitive data of
   the network providers.  Hence, certain action may take to anonymize
   the telemetry data and only convey the relative ratio in rate
   adaptation across ASes without revealing the actual network load.

   Another consideration is the security of receiving telemetry
   information.  The rate adaptation mechanism in HPCC++ relies on
   feedback from the network.  As such, it is vulnerable to attacks
   where feedback messages are hijacked, replaced, or intentionally
   injected with misleading information resulting in denial of service,
   similar to those that can affect TCP.  It is therefore RECOMMENDED
   that the notification feedback message is at least integrity checked.
   In addition, [I-D.ietf-avtcore-cc-feedback-message] discusses the
   potential risk of a receiver providing misleading congestion feedback
   information and the mechanisms for mitigating such risks.

9.2.  Switch-assisted congestion control

   HPCC++ falls in the general category of switch-assisted congestion
   control.  However, HPCC++ includes a few unique design choices that
   are different from other switch-assisted approaches.

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   *  First, HPCC++ implements a primal-mode algorithm that requires
      only the ``write-to-packet'' operation from switches, which has
      already been supported by telemetry protocols like INT [P4-INT] or
      IOAM [I-D.ietf-ippm-ioam-data].  Please note that this is very
      different from dual-mode algorithms such as XCP
      [Katabi-SIGCOMM2002] and RCP [Dukkipati-RCP], where switches take
      an actively role in determining flows' rates.

   *  Second, HPCC++ achieves a fast utilization convergence by
      decoupling it from fairness convergence, which is inspired by XCP.

   *  Third, HPCC++ enables the switch-guided multiplicative increase
      (MI) by defining the ``inflight byte'' to quantify the link load.
      The inflight byte tells both the underload and overload of the
      link precisely and thus it allows the flow to increase/decrease
      the rate multiplicatively and safely.  By contrast, traditional
      approaches of using the queue length or RTT as the feedback cannot
      guide the rate increase and instead have to rely on additive
      increase (AI) with heuristics.  As the link speed contines to
      grow, this becomes increasingly slow in reclaiming the unused
      bandwidth.  Besides, queue-based feedback mechanisms subject to
      latency inflation.

   *  Last, HPCC++ uses TX rate instead of RX rate used by XCP and RCP.
      As detailed in [SIGCOMM-HPCC], we view the TX rate is more precise
      because RX rate and queue length are overlapped and thus it causes

9.3.  Work with transport protocols

   HPCC++ can be adopted as the CC algorithm by a wide range of
   transport protocols such as TCP and UDP, as well as others that may
   run on top of them, such as iWARP, RoCE etc.  It requires to have the
   window limit and congestion feedback through ACK self-clocking, which
   naturally conforms to the paradigm of TCP design.  With that, HPCC++
   introduces a scheme to measure the total inflight bytes for more
   precise congestion control.  To run in UDP, some modifications need
   to be done to enforce the window limit and collect congestion
   feedback via probing packets, which is incremental.

9.4.  Work with QoS queuing

   Under the use of QoS (Quality of service) priority queuing in
   switches, the length of flow's own queue cannot tell the actual
   queuing time and the exact extent of congestion.  Although general
   approaches for running congestion control with QoS queuing are out of
   the scope of this document, we provide a few hints for HPCC++ running
   friendly with QoS queuing.  In this case, HPCC++ can leverage the

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   packet sojourn time (the egress timestamp minus the ingress
   timestamp) instead of the queue length to quantify the packet's
   actual queuing delay.  In addition, the operators typically use the
   Deficit Weighted Round Robin (DWRR) instead of the strict priority
   (SP) as their QoS scheduling to prevent traffic starvation.  DWRR
   provides a minimum bandwdith guarantee for each queue so that HPCC++
   can leverage it for precise rate update to avoid congestion.

10.  Acknowledgments

   The authors would like to thank ICCRG members for their valuable
   review comments and helpful input to this specification.

11.  Contributors

   The following individuals have contributed to the implementation and
   evaluation of the proposed scheme, and therefore have helped to
   validate and substantially improve this specification: Pedro Y.
   Segura, Roberto P.  Cebrian, Robert Southworth and Malek Musleh.

12.  References

12.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <>.

12.2.  Informative References

              Sarker, Z., Perkins, C., Singh, V., and M. A. Ramalho,
              "RTP Control Protocol (RTCP) Feedback for Congestion
              Control", Work in Progress, Internet-Draft, draft-ietf-
              avtcore-cc-feedback-message-09, 2 November 2020,

              Katabi, D., Handley, M., and C. Rohrs, "Congestion Control
              for High Bandwidth-Delay Product Networks", ACM
              SIGCOMM Pittsburgh, Pennsylvania, USA, October 2002.

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              Zhu, Y., Eran, H., Firestone, D., Guo, C., Lipshteyn, M.,
              Liron, Y., Padhye, J., Raindel, S., Yahia, M. H., and M.
              Zhang, "Congestion Control for Large-Scale RDMA
              Deployments", ACM SIGCOMM London, United Kingdom, August

   [P4-INT]   "In-band Network Telemetry (INT) Dataplane Specification,
              v2.0", February 2020, <

              "Data Fields for In-situ OAM", March 2020,

              "Inband Flow Analyzer", February 2019,

              Li, Y., Miao, R., Liu, H., Zhuang, Y., Fei Feng, F., Tang,
              L., Cao, Z., Zhang, M., Kelly, F., Alizadeh, M., and M.
              Yu, "HPCC: High Precision Congestion Control", ACM
              SIGCOMM Beijing, China, August 2019.

              Dukkipati, N., "Rate Control Protocol (RCP): Congestion
              control to make flows complete quickly.", Stanford
              University , 2008.

Authors' Addresses

   Rui Miao
   Alibaba Group
   525 Almanor Ave, 4th Floor
   Sunnyvale, CA 94085
   United States of America


   Hongqiang H. Liu
   Alibaba Group
   108th Ave NE, Suite 800
   Bellevue, WA 98004
   United States of America

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   Rong Pan
   Intel, Corp.
   2200 Mission College Blvd.
   Santa Clara, CA 95054
   United States of America


   Jeongkeun Lee
   Intel, Corp.
   4750 Patrick Henry Dr.
   Santa Clara, CA 95054
   United States of America


   Changhoon Kim
   Intel Corporation
   4750 Patrick Henry Dr.
   Santa Clara, CA 95054
   United States of America


   Barak Gafni
   Mellanox Technologies, Inc.
   350 Oakmead Parkway, Suite 100
   Sunnyvale, CA 94085
   United States of America


   Yuval Shpigelman
   Mellanox Technologies, Inc.
   Haim Hazaz 3A
   Netanya 4247417


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   Jeff Tantsura
   Microsoft Corporation
   One Microsoft Way
   Redmond, Washington 98052-6399
   United States of America


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