



Network Working Group                                    B. Briscoe, Ed.
Internet-Draft                                               Independent
Intended status: Informational                                  G. White
Expires: 14 November 2022                                      CableLabs
                                                             13 May 2022


     The DOCSIS® Queue Protection Algorithm to Preserve Low Latency
                  draft-briscoe-docsis-q-protection-06

Abstract

   This informational document explains the specification of the queue
   protection algorithm used in DOCSIS technology since version 3.1.  A
   shared low latency queue relies on the non-queue-building behaviour
   of every traffic flow using it.  However, some flows might not take
   such care, either accidentally or maliciously.  If a queue is about
   to exceed a threshold level of delay, the queue protection algorithm
   can rapidly detect the flows most likely to be responsible.  It can
   then prevent harm to other traffic in the low latency queue by
   ejecting selected packets (or all packets) of these flows.  The
   document is designed for four types of audience: a) congestion
   control designers who need to understand how to keep on the 'good'
   side of the algorithm; b) implementers of the algorithm who want to
   understand it in more depth; c) designers of algorithms with similar
   goals, perhaps for non-DOCSIS scenarios; and d) researchers
   interested in evaluating the algorithm.

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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   Internet-Drafts are draft documents valid for a maximum of six months
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   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 14 November 2022.







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Copyright Notice

   Copyright (c) 2022 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.  Code Components
   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Document Roadmap  . . . . . . . . . . . . . . . . . . . .   4
     1.2.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   5
     1.3.  Copyright Material  . . . . . . . . . . . . . . . . . . .   5
   2.  Approach - In Brief . . . . . . . . . . . . . . . . . . . . .   5
     2.1.  Mechanism . . . . . . . . . . . . . . . . . . . . . . . .   6
     2.2.  Policy  . . . . . . . . . . . . . . . . . . . . . . . . .   7
       2.2.1.  Policy Conditions . . . . . . . . . . . . . . . . . .   7
       2.2.2.  Policy Action . . . . . . . . . . . . . . . . . . . .   7
   3.  Necessary Flow Behaviour  . . . . . . . . . . . . . . . . . .   7
   4.  Pseudocode Walk-Through . . . . . . . . . . . . . . . . . . .   8
     4.1.  Input Parameters, Constants and Variables . . . . . . . .   9
     4.2.  Queue Protection Data Path  . . . . . . . . . . . . . . .  12
       4.2.1.  The qprotect() function . . . . . . . . . . . . . . .  13
       4.2.2.  The pick_bucket() function  . . . . . . . . . . . . .  14
       4.2.3.  The fill_bucket() function  . . . . . . . . . . . . .  17
       4.2.4.  The calcProbNative() function . . . . . . . . . . . .  17
   5.  Rationale . . . . . . . . . . . . . . . . . . . . . . . . . .  18
     5.1.  Rationale: Blame for Queuing, not for Rate in Itself  . .  18
     5.2.  Rationale for Aging the Queuing Score . . . . . . . . . .  20
     5.3.  Rationale for Transformed Queuing Score . . . . . . . . .  21
     5.4.  Rationale for Policy Conditions . . . . . . . . . . . . .  22
     5.5.  Rationale for Reclassification as the Policy Action . . .  25
   6.  Limitations . . . . . . . . . . . . . . . . . . . . . . . . .  26
   7.  IANA Considerations (to be removed by RFC Editor) . . . . . .  26
   8.  Implementation Status . . . . . . . . . . . . . . . . . . . .  26
   9.  Security Considerations . . . . . . . . . . . . . . . . . . .  27
     9.1.  Resource Exhaustion Attacks . . . . . . . . . . . . . . .  28
       9.1.1.  Exhausting Flow-State Storage . . . . . . . . . . . .  28
       9.1.2.  Exhausting Processing Resources . . . . . . . . . . .  29
   10. Comments Solicited  . . . . . . . . . . . . . . . . . . . . .  29
   11. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  29



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   12. References  . . . . . . . . . . . . . . . . . . . . . . . . .  30
     12.1.  Normative References . . . . . . . . . . . . . . . . . .  30
     12.2.  Informative References . . . . . . . . . . . . . . . . .  31
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  32

1.  Introduction

   This informational document explains the specification of the queue
   protection (QProt) algorithm used in DOCSIS technology since version
   3.1 [DOCSIS].

   Although the algorithm is defined in annex P of [DOCSIS], it relies
   on cross-references to other parts of the set of specs.  This
   document pulls all the strands together into one self-contained
   document.  The core of the document is a similar pseudocode walk-
   through to that in the DOCSIS spec, but it also includes additional
   material: i) a brief overview; ii) a definition of how a data sender
   needs to behave to avoid triggering queue protection; and iii) a
   section giving the rationale for the design choices.

   Low queuing delay depends on hosts sending their data smoothly,
   either at low rate or responding to explicit congestion notifications
   (ECN [RFC8311], [I-D.ietf-tsvwg-ecn-l4s-id]).  So low queuing latency
   is something hosts create themselves, not something the network gives
   them.  This tends to ensure that self-interest alone does not drive
   flows to mis-mark their packets for the low latency queue.  However,
   traffic from an application that does not behave in a non-queue-
   building way might erroneously be classified into a low latency
   queue, whether accidentally or maliciously.  QProt protects other
   traffic in the low latency queue from the harm due to excess queuing
   that would otherwise be caused by such anomalous behaviour.

   In normal scenarios without misclassified traffic, QProt is not
   expected to intervene at all in the classification or forwarding of
   packets.

   An overview of how low latency support has been added to DOCSIS
   technology is given in [LLD].  In each direction of a DOCSIS link
   (upstream and downstream), there are two queues: one for Low Latency
   (LL) and one for Classic traffic, in an arrangement similar to the
   IETF's Coupled DualQ AQM [I-D.ietf-tsvwg-aqm-dualq-coupled].  The two
   queues enable a transition from 'Classic' to 'Scalable' congestion
   control so that low latency can become the norm for any application,
   including ones seeking both full throughput and low latency, not just
   low-rate applications that have been more traditionally associated
   with a low latency service.  The Classic queue is only necessary for
   traffic such as traditional (Reno/Cubic) TCP that needs about a round
   trip of buffering to fully utilize the link, and therefore has no



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   incentive to mismark itself as low latency.  The QProt function is
   located at the ingress to the Low Latency queue.  Therefore, in the
   upstream QProt is located on the cable modem (CM), and in the
   downstream it is located on the cable CMTS (CM Termination System).
   If an arriving packet triggers queue protection, the QProt algorithm
   ejects the packet from the Low Latency queue and reclassifies it into
   the Classic queue.

   If QProt is used in settings other than DOCSIS links, it would be a
   simple matter to detect queue-building flows by using slightly
   different conditions, and/or to trigger a different action as a
   consequence, as appropriate for the scenario, e.g., dropping instead
   of reclassifying packets or perhaps accumulating a second per-flow
   score to decide whether to redirect a whole flow rather than just
   certain packets.  Such work is for future study and out of scope of
   the present document.

   The algorithm is based on a rigorous approach to quantifying how much
   each flow contributes to congestion, which is used in economics to
   allocate responsibility for the cost of one party's behaviour on
   others (the economic externality).  Another important feature of the
   approach is that the metric used for the queuing score is based on
   the same variable that determines the level of ECN signalling seen by
   the sender [RFC8311], [I-D.ietf-tsvwg-ecn-l4s-id].  This makes the
   internal queuing score visible externally as ECN markings.  This
   transparency is necessary to be able to objectively state (in
   Section 3) how a flow can keep on the 'good' side of the algorithm.

1.1.  Document Roadmap

   The core of the document is the walk-through of the DOCSIS QProt
   algorithm's pseudocode in Section 4.

   Prior to that, Section 2 summarizes the approach used in the
   algorithm, then Section 3 considers QProt from the perspective of the
   end-system, by defining the behaviour that a flow needs to comply
   with to avoid the QProt algorithm ejecting its packets from the low
   latency queue.

   Section 5 gives deeper insight into the principles and rationale
   behind the algorithm.  Then Section 6 explains the limitations of the
   approach, followed by the usual closing sections.









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1.2.  Terminology

   The normative language for the DOCSIS QProt algorithm is in the
   DOCSIS specs [DOCSIS], [DOCSIS-CM-OSS], [DOCSIS-CCAP-OSS] not in this
   informational guide.  If there is any inconsistency, the DOCSIS specs
   take precedence.

   The following terms and abbreviations are used:

   CM:  Cable Modem

   CMTS:  CM Termination System

   Congestion-rate:  The transmission rate of bits or bytes contained
      within packets of a flow that have the CE codepoint set in the IP-
      ECN field [RFC3168] (including IP headers unless specified
      otherwise).  Congestion-bit-rate and congestion-volume were
      introduced in [RFC7713] and [RFC6789].

   DOCSIS:  Data Over Cable System Interface Specification.  "DOCSIS" is
      a registered trademark of Cable Television Laboratories, Inc.
      ("CableLabs").

   Non-queue-building:  A flow that tends not to build a queue

   Queue-building:  A flow that builds a queue.  If it is classified
      into the Low Latency queue, it is therefore a candidate for the
      queue protection algorithm to detect and sanction.

   ECN:  Explicit Congestion Notification

   QProt:  The Queue Protection function

1.3.  Copyright Material

   Parts of this document are reproduced from [DOCSIS] with kind
   permission of the copyright holder, Cable Television Laboratories,
   Inc. ("CableLabs").

2.  Approach - In Brief

   The algorithm is divided into mechanism and policy.  There is only a
   tiny amount of policy code, but policy might need to be changed in
   the future.  So, where hardware implementation is being considered,
   it would be advisable to implement the policy aspects in firmware or
   software:





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   *  The mechanism aspects identify flows, maintain flow-state and
      accumulate per-flow queuing scores;

   *  The policy aspects can be divided into conditions and actions:

      -  The conditions are the logic that determines when action should
         be taken to avert the risk of queuing delay becoming excessive;

      -  The actions determine how this risk is averted, e.g., by
         redirecting packets from a flow into another queue, or to
         reclassify a whole flow that seems to be misclassified.

2.1.  Mechanism

   The algorithm maintains per-flow-state, where 'flow' usually means an
   end-to-end (layer-4) 5-tuple.  The flow-state consists of a queuing
   score that decays over time.  Indeed it is transformed into time
   units so that it represents the flow-state's own expiry time
   (explained in Section 5.3).  A higher queuing score pushes out the
   expiry time further.

   Non-queue-building flows tend to release their flow-state rapidly ---
   it usually expires reasonably early in the gap between the packets of
   a normal flow.  Then the memory can be recycled for packets from
   other flows that arrive in between.  So only queue-building flows
   hold flow state persistently.

   The simplicity and effectiveness of the algorithm is due to the
   definition of the queuing score.  The queueing score represents the
   share of blame for queuing that each flow bears.  The scoring
   algorithm uses the same internal variable, probNative, that the AQM
   for the low latency queue uses to ECN-mark packets (the other two
   forms of marking, Classic and coupled, are driven by Classic traffic
   and therefore not relevant to protection of the LL queue).  In this
   way, the queuing score accumulates the size of each arriving packet
   of a flow, but scaled by the value of probNative (in the range 0 to
   1) at the instant the packet arrives.  So a flow's score accumulates
   faster, the higher the degree of queuing and the faster that the
   flow's packets arrive when there is queuing.  Section 5.1 explains
   further why this score represents blame for queuing.

   The algorithm as described so far would accumulate a number that
   would rise at the so-called congestion-rate of the flow (see
   Terminology in Section 1.2), i.e., the rate at which the flow is
   contributing to congestion, or the rate at which the AQM is
   forwarding bytes of the flow that are ECN marked.  However, rather
   than growing continually, the queuing score is also reduced (or
   'aged') at a constant rate.  This is because it is unavoidable for



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   capacity-seeking flows to induce a continuous low level of congestion
   as they track available capacity.  Section 5.2 explains why this
   allowance can be set to the same constant for any scalable flow,
   whatever its bit rate.

   For implementation efficiency, the queuing score is transformed into
   time units so that it represents the expiry time of the flow state
   (as already discussed above).  Then it does not need to be explicitly
   aged, because the natural passage of time implicitly 'ages' an expiry
   time.  The transformation into time units simply involves dividing
   the queuing score of each packet by the constant aging rate
   (explained further in Section 5.3).

2.2.  Policy

2.2.1.  Policy Conditions

   The algorithm uses the queuing score to determine whether to eject
   each packet only at the time it first arrives.  This limits the
   policies available.  For instance, when queueing delay exceeds a
   threshold, it is not possible to eject a packet from the flow with
   the highest queuing scoring, because that would involve searching the
   queue for such a packet (if indeed one was still in the queue).
   Nonetheless, it is still possible to develop a policy that protects
   the low latency of the queue by making the queuing score threshold
   stricter the greater the excess of queuing delay relative to the
   threshold (explained in Section 5.4).

2.2.2.  Policy Action

   In the DOCSIS QProt spec at the time of writing, when the policy
   conditions are met the action taken to protect the low latency queue
   is to reclassify a packet into the Classic queue (justified in
   Section 5.5).

3.  Necessary Flow Behaviour

   The QProt algorithm described here can be used for responsive and/or
   unresponsive flows.

   *  It is possible to objectively describe the least responsive way
      that a flow will need to respond to congestion signals in order to
      avoid triggering queue protection, no matter the link capacity and
      no matter how much other traffic there is.

   *  It is not possible to describe how fast or smooth an unresponsive
      flow should be to avoid queue protection, because this depends on
      how much other traffic there is and the capacity of the link,



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      which an application is unable to know.  However, the more
      smoothly an unresponsive flow paces its packets and the lower its
      rate relative to typical broadband link capacities, the less
      likelihood that it will risk causing enough queueing to trigger
      queue protection.

   Responsive low latency flows can use an L4S ECN codepoint
   [I-D.ietf-tsvwg-ecn-l4s-id] to get classified into the low latency
   queue.

   A sender can arrange for flows that are smooth but do not respond to
   ECN marking to be classified into the low latency queue by using the
   Non-Queue-Building (NQB) Diffserv codepoint [I-D.ietf-tsvwg-nqb],
   which the DOCSIS specs support, or an operator can use various other
   local classifiers.

   As already explained in Section 2.1, the QProt algorithm is driven
   from the same variable that drives the ECN marking probability in the
   low latency queue (see the Immediate Active Queue Management Annex in
   [DOCSIS]).  The algorithm that calculates this internal variable is
   run on the arrival of every packet, whether it is ECN-capable or not,
   so that it can be used by the QProt algorithm.  But the variable is
   only used to ECN-mark packets that are ECN-capable.

   Not only does this dual use of the variable improve processing
   efficiency, but it also makes the basis of the QProt algorithm
   visible and transparent, at least for responsive ECN-capable flows.
   Then it is possible to state objectively that a flow can avoid
   triggering queue protection by keeping the bit rate of ECN marked
   packets (the congestion-rate) below AGING, which is a configured
   constant of the algorithm (default 2^19 B/s ~= 4 Mb/s).  Note that it
   is in a congestion controller's own interest to keep its average
   congestion-rate well below this level (e.g., ~1 Mb/s), to ensure that
   it does not trigger queue protection during transient dynamics.

   If the QProt algorithm is used in other settings, it would still need
   to be based on the visible level of congestion signalling, in a
   similar way to the DOCSIS approach.  Without transparency of the
   basis of the algorithm's decisions, end-systems would not be able to
   avoid triggering queue protection on an objective basis.

4.  Pseudocode Walk-Through









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4.1.  Input Parameters, Constants and Variables

   The operator input parameters that set the parameters in the first
   two blocks of pseudocode below are defined for cable modems (CMs) in
   [DOCSIS-CM-OSS] and for CMTSs in [DOCSIS-CCAP-OSS].  Then, further
   constants are either derived from the input parameters or hard-coded.

   Defaults and units are shown in square brackets.  Defaults (or indeed
   any aspect of the algorithm) are subject to change, so the latest
   DOCSIS specs are the definitive references.  Also any operator might
   set certain parameters to non-default values.

   <CODE BEGINS>
   // Input Parameters
   MAX_RATE;          // Configured maximum sustained rate [b/s]
   QPROTECT_ON;       // Queue Protection is enabled [Default: TRUE]
   CRITICALqL_us;     // L queue threshold delay [us] Default: MAXTH_us
   CRITICALqLSCORE_us;// The threshold queuing score [Default: 4000us]
   LG_AGING;          // The aging rate of the q'ing score [Default: 19]
                      //  as log base 2 of the congestion-rate [lg(B/s)]

   // Input Parameters for the calcProbNative() algorithm:
   MAXTH_us;          // Max IAQM marking threshold [Default: 1000us]
   LG_RANGE;          // Log base 2 of the range of ramp [lg(ns)]
                      //  Default: 2^19 = 524288 ns (roughly 525 us)
   <CODE ENDS>

























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   <CODE BEGINS>
   // Constants, either derived from input parameters or hard-coded
   T_RES;                                    // Resolution of t_exp [ns]
                                             // Convert units (approx)
   AGING = pow(2, (LG_AGING-30) ) * T_RES;   // lg([B/s]) to [B/T_RES]
   CRITICALqL = CRITICALqL_us * 1000;        // [us] to [ns]
   CRITICALqLSCORE = CRITICALqLSCORE_us * 1000/T_RES; // [us] to [T_RES]
   // Threshold for the q'ing score condition
   CRITICALqLPRODUCT = CRITICALqL * CRITICALqLSCORE;
   qLSCORE_MAX = 5E9 / T_RES;           // Max queuing score = 5 s

   ATTEMPTS = 2; // Max attempts to pick a bucket (vendor-specific)
   BI_SIZE = 5;  // Bit-width of index number for non-default buckets
   NBUCKETS = pow(2, BI_SIZE);  // No. of non-default buckets
   MASK = NBUCKETS-1;     // convenient constant, with BI_SIZE LSBs set

                          // Queue Protection exit states
   EXIT_SUCCESS  = 0;     // Forward the packet
   EXIT_SANCTION = 1;     // Redirect the packet

   MAX_PROB      = 1; // For integer arithmetic, would use a large int
                      //  e.g., 2^31, to allow space for overflow
   MAXTH = MAXTH_us * 1000;   // Max marking threshold [ns]
   MAX_FRAME_SIZE = 2000;  // DOCSIS-wide constant [B]
   // Minimum marking threshold of 2 MTU for slow links [ns]
   FLOOR =  2 * 8 * MAX_FRAME_SIZE * 10^9 / MAX_RATE;
   RANGE = (1 << LG_RANGE);      // Range of ramp [ns]
   MINTH = max ( MAXTH - RANGE, FLOOR);
   MAXTH = MINTH + RANGE;           // Max marking threshold [ns]
   <CODE ENDS>

   Throughout the pseudocode, most variables are integers.  The only
   exceptions are floating point variables representing probabilities
   (MAX_PROB and probNative) and the AGING parameter.  The actual DOCSIS
   QProt algorithm is defined using integer arithmetic, but in the
   floating point arithmetic used in this document, (0 <= probNative <=
   1).  Also, the pseudocode omits overflow checking and it would need
   to be made robust to non-default input parameters.

   The resolution for expressing time, T_RES, needs to be chosen to
   ensure that expiry times for buckets can represent times that are a
   fraction (e.g., 1/10) of the expected packet interarrival time for
   the system.

   The following definitions explain the purpose of important variables
   and functions.





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   <CODE BEGINS>
   // Public variables:
   qdelay;        // The current queuing delay of the LL queue [ns]
   probNative;    // Native marking probability of LL queue within [0,1]

   // External variables
   packet;            // The structure holding packet header fields
   packet.size;       // The size of the current packet [B]
   packet.uflow;      // The flow identifier of the current packet
                      //  (e.g., 5-tuple or 4-tuple if IPSec)

   // Irrelevant details of DOCSIS function to return qdelay are removed
   qdelayL(...)      // Returns current delay of the low latency Q [ns]
   <CODE ENDS>

   Pseudocode for how the algorithm categorizes packets by flow ID to
   populate the variable packet.uflow is not given in detail here.  The
   application's flow ID is usually defined by a common 5-tuple (or
   4-tuple) of:

   *  source and destination IP addresses of the innermost IP header
      found;

   *  the protocol (IPv4) or next header (IPv6) field in this IP header

   *  either of:

      -  source and destination port numbers, for TCP, UDP, UDP-Lite,
         SCTP, DCCP, etc.

      -  Security Parameters Index (SPI) for IPSec Encapsulating
         Security Payload (ESP) [RFC4303].

   The Microflow Classification section of the Queue Protection Annex of
   the DOCSIS spec. [DOCSIS] defines various strategies to find these
   headers by skipping extension headers or encapsulations.  If they
   cannot be found, the spec. defines various less-specific 3-tuples
   that would be used.  The DOCSIS spec. should be referred to for all
   these strategies, which will not be repeated here.

   The array of bucket structures defined below is used by all the Queue
   Protection functions:









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   <CODE BEGINS>
   struct bucket { // The leaky bucket structure to hold per-flow state
      id;          // identifier (e.g., 5-tuple) of flow using bucket
      t_exp;       // expiry time in units of T_RES
                   // (t_exp - now) = flow's transformed q'ing score
   };
   struct bucket buckets[NBUCKETS+1];
   <CODE ENDS>

4.2.  Queue Protection Data Path

   All the functions of Queue Protection operate on the data path,
   driven by packet arrivals.

   The following functions that maintain per-flow queuing scores and
   manage per-flow state are considered primarily as mechanism:

      pick_bucket(uflow_id); // Returns bucket identifier

      fill_bucket(bucket_id, pkt_size, probNative); // Returns queuing
      score

      calcProbNative(qdelay) // Returns probability of ECN-marking

   The following function is primarily concerned with policy:

      qprotect(packet, ...); // Returns exit status to either forward or
      redirect the packet

   ('...' suppresses distracting detail.)

   Future modifications to policy aspects are more likely than to
   mechanisms.  Therefore, policy aspects would be less appropriate
   candidates for any hardware acceleration.

   The entry point to these functions is qprotect(), which is called
   from packet classification before each packet is enqueued into the
   appropriate queue, queue_id, as follows:













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   <CODE BEGINS>
   classifier(packet) {
      // Determine which queue using ECN, DSCP and any local-use fields
      queue_id = classify(packet);
      //  LQ & CQ are macros for valid queue IDs returned by classify()
      if (queue_id == LQ) {
         // if packet classified to Low Latency Service Flow
         if (QPROTECT_ON) {
            if (qprotect(packet, ...) == EXIT_SANCTION) {
               // redirect packet to Classic Service Flow
               queue_id = CQ;
            }
         }
      return queue_id;
   }
   <CODE ENDS>

4.2.1.  The qprotect() function

   On each packet arrival, qprotect() measures the current queue delay
   and derives the native marking probability from it.  Then it uses
   pick_bucket to find the bucket already holding the flow's state, or
   to allocate a new bucket if the flow is new or its state has expired
   (the most likely case).  Then the queuing score is updated by the
   fill_bucket() function.  That completes the mechanism aspects.

   The comments against the subsequent policy conditions and actions
   should be self-explanatory at a superficial level.  The deeper
   rationale for these conditions is given in Section 5.4.






















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   <CODE BEGINS>
   // Per packet queue protection
   qprotect(packet, ...) {

      bckt_id;   // bucket index
      qLscore;   // queuing score of pkt's flow in units of T_RES

      qdelay = qL.qdelay(...);
      probNative = calcProbNative(qdelay);

      bckt_id = pick_bucket(packet.uflow);
      qLscore = fill_bucket(buckets[bckt_id], packet.size, probNative);

      // Determine whether to sanction packet
      if ( ( ( qdelay > CRITICALqL ) // Test if qdelay over threshold...
         // ...and if flow's q'ing score scaled by qdelay/CRITICALqL
         // ...exceeds CRITICALqLSCORE
         && ( qdelay * qLscore > CRITICALqLPRODUCT ) )
         // or qLSCORE_MAX reached
         || ( qLscore >= qLSCORE_MAX ) )

         return EXIT_SANCTION;

      else
         return EXIT_SUCCESS;
   }
   <CODE ENDS>

4.2.2.  The pick_bucket() function

   The pick_bucket() function is optimized for flow-state that will
   normally have expired from packet to packet of the same flow.  It is
   just one way of finding the bucket associated with the flow ID of
   each packet - it might be possible to develop more efficient
   alternatives.

   The algorithm is arranged so that the bucket holding any live (non-
   expired) flow-state associated with a packet will always be found
   before a new bucket is allocated.  The constant ATTEMPTS, defined
   earlier, determines how many hashes are used to find a bucket for
   each flow (actually, only one hash is generated; then, by default, 5
   bits of it at a time are used as the hash value, because by default
   there are 2^5 = 32 buckets).








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   The algorithm stores the flow's own ID in its flow-state.  So, when a
   packet of a flow arrives, the algorithm tries up to ATTEMPTS times to
   hash to a bucket, looking for the flow's own ID.  If found, it uses
   that bucket, first resettings the expiry time to 'now' if it has
   expired.

   If it does not find the flow's ID, and the expiry time is still
   current, the algorithm can tell that another flow is using that
   bucket, and it continues to look for a bucket for the flow.  Even if
   it finds another flow's bucket where the expiry time has passed, it
   doesn't immediately use it.  It merely remembers it as the potential
   bucket to use.  But first it runs through all the ATTEMPTS hashes to
   look for a bucket assigned to the flow ID.  Then, if a live bucket is
   not already associated with the packet's flow, the algorithm should
   have already set aside an existing bucket with a score that has aged
   out.  Given this bucket is no longer necessary to hold state for its
   previous flow, it can be recycled for use by the present packet's
   flow.

   If all else fails, there is one additional bucket (called the dregs)
   that can be used.  If the dregs is still in live use by another flow,
   subsequent flows that cannot find a bucket of their own all share it,
   adding their score to the one in the dregs.  A flow might get away
   with using the dregs on its own, but when there are many mis-marked
   flows, multiple flows are more likely to collide in the dregs,
   including innocent flows.  The choice of number of buckets and number
   of hash attempts determines how likely it will be that this
   undesirable scenario will occur.























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   <CODE BEGINS>
   // Pick the bucket associated with flow uflw
   pick_bucket(uflw) {

      now;                      // current time
      j;                        // loop counter
      h32;                      // holds hash of the packet's flow IDs
      h;                        // bucket index being checked
      hsav;                     // interim chosen bucket index

      h32   = hash32(uflw);     // 32-bit hash of flow ID
      hsav  = NBUCKETS;         // Default bucket
      now   = get_time_now();   // in units of T_RES

      // The for loop checks ATTEMPTS buckets for ownership by flow-ID
      // It also records the 1st bucket, if any, that could be recycled
      // because it's expired.
      // Must not recycle a bucket until all ownership checks completed
      for (j=0; j<ATTEMPTS; j++) {
         // Use least signif. BI_SIZE bits of hash for each attempt
         h = h32 & MASK;
         if (buckets[h].id == uflw) {    // Once uflw's bucket found...
            if (buckets[h].t_exp <= now) // ...if bucket has expired...
               buckets[h].t_exp = now;   // ...reset it
            return h;                    // Either way, use it
         }
         else if ( (hsav == NBUCKETS)  // If not seen expired bucket yet
                                       //  and this bucket has expired
              && (buckets[h].t_exp <= now) ) {
            hsav = h;                  // set it as the interim bucket
         }
         h32 >>= BI_SIZE;          // Bit-shift hash for next attempt
      }
      // If reached here, no tested bucket was owned by the flow-ID
      if (hsav != NBUCKETS) {
         // If here, found an expired bucket within the above for loop
         buckets[hsav].t_exp = now;              // Reset expired bucket
      } else {
         // If here, we're having to use the default bucket (the dregs)
         if (buckets[hsav].t_exp <= now) {   // If dregs has expired...
            buckets[hsav].t_exp = now;       // ...reset it
         }
      }
      buckets[hsav].id = uflw; // In either case, claim for recycling
      return hsav;
   }
   <CODE ENDS>




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4.2.3.  The fill_bucket() function

   The fill_bucket() function both accumulates and ages the queuing
   score over time, as outlined in Section 2.1.  To make aging the score
   efficient, the increment of the queuing score is transformed into
   units of time by dividing by AGING, so that the result represents the
   new expiry time of the flow.

   Given that probNative is already used to select which packets to ECN-
   mark, it might be thought that the queuing score could just be
   incremented by the full size of each selected packet, instead of
   incrementing it by the product of every packet's size (pkt_sz) and
   probNative.  However, the unpublished experience of one of the
   authors with other congestion policers has found that the score then
   increments far too jumpily, particularly when probNative is low.

   A deeper explanation of the queuing score is given in Section 5.

   <CODE BEGINS>
   fill_bucket(bckt_id, pkt_sz, probNative) {
      now;                                       // current time
      now = get_time_now();                      // in units of T_RES
      // Add packet's queuing score
      // For integer arithmetic, a bit-shift can replace the division
      qLscore = min(buckets[bckt_id].t_exp - now
                    + probNative * pkt_sz / AGING, qLSCORE_MAX);
      buckets[bckt_id].t_exp = now + qLscore;
      return qLscore;
   }
   <CODE ENDS>

4.2.4.  The calcProbNative() function

   To derive this queuing score, the QProt algorithm uses the linear
   ramp function calcProbNative() to normalize instantaneous queuing
   delay into a probability in the range [0,1], which it assigns to
   probNative.














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   <CODE BEGINS>
   calcProbNative(qdelay){
         if ( qdelay >= MAXTH ) {
            probNative = MAX_PROB;
         } else if ( qdelay > MINTH ) {
            probNative = MAX_PROB * (qdelay - MINTH)/RANGE;
            // In practice, the * and the / would use a bit-shift
         } else {
            probNative = 0;
         }
         return probNative;
   }
   <CODE ENDS>

5.  Rationale


5.1.  Rationale: Blame for Queuing, not for Rate in Itself

   Figure 1 shows the bit rates of two flows as stacked areas.  It poses
   the question of which flow is more to blame for queuing delay; the
   unresponsive constant bit rate flow (c) that is consuming about 80%
   of the capacity, or the flow sending regular short unresponsive
   bursts (b)?  The smoothness of c seems better for avoiding queuing,
   but its high rate does not.  However, if flow c was not there, or ran
   slightly more slowly, b would not cause any queuing.

   ^ bit rate (stacked areas)
   |  ,-.          ,-.          ,-.          ,-.          ,-.
   |--|b|----------|b|----------|b|----------|b|----------|b|---Capacity
   |__|_|__________|_|__________|_|__________|_|__________|_|_____
   |
   |                       c
   |
   |
   |
   +---------------------------------------------------------------->
                                                                 time

            Figure 1: Which is More to Blame for Queuing Delay?

   To explain queuing scores, in the following it will initially be
   assumed that the QProt algorithm is accumulating queuing scores, but
   not taking any action as a result.

   To quantify the responsibility that each flow bears for queuing
   delay, the QProt algorithm accumulates the product of the rate of
   each flow and the level of congestion, both measured at the instant



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   each packet arrives.  The instantaneous flow rate is represented at
   each discrete event when a packet arrives by the packet's size, which
   accumulates faster the more packets arrive within each unit of time.
   The level of congestion is normalized to a dimensionless number
   between 0 and 1 (probNative).  This fractional congestion level is
   used in preference to a direct dependence on queuing delay for two
   reasons:

   *  to be able to ignore very low levels of queuing that contribute
      insignificantly to delay

   *  to be able to erect a steep barrier against excessive queuing
      delay

   The unit of the resulting queue score is "congested-bytes" per
   second, which distinguishes it from just bytes per second.

   Then, during the periods between bursts (b), neither flow accumulates
   any queuing score - the high rate of c is benign.  But, during each
   burst, if we say the rate of c and b are 80% and 45% of capacity,
   thus causing 25% overload, they each bear (80/125)% and (45/125)% of
   the responsibility for the queuing delay (64% and 36%).  The
   algorithm does not explicitly calculate these percentages.  They are
   just the outcome of the number of packets arriving from each flow
   during the burst.

   To summarize, the queuing score never sanctions rate solely on its
   own account.  It only sanctions rate inasmuch as it causes queuing.

   ^ bit rate (stacked areas)                               ,
   |               ,-.                       |\           ,-
   |------Capacity-|b|----------,-.----------|b|----------|b\-----
   |             __|_|_______   |b|        /``\| _...-._-': | ,.--
   |  ,-.     __/            \__|_|_     _/    |/          \|/
   |  |b| ___/                      \___/   __       r
   |  |_|/                v             \__/  \_______    _/\____/
   | _/                                               \__/
   |
   +---------------------------------------------------------------->
                                                                 time

        Figure 2: Responsibility for Queuing: More Complex Scenario

   Figure 2 gives a more complex illustration of the way the queuing
   score assigns responsibility for queuing (limited to the precision
   that ASCII art can illustrate).  The figure shows the bit rates of
   three flows represented as stacked areas labelled b, v and r.  The
   unresponsive bursts (b) are the same as in the previous example, but



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   a variable rate video (v) replaces flow c.  It's rate varies as the
   complexity of the video scene varies.  Also on a slower timescale, in
   response to the level of congestion, the video adapts its quality.
   However, on a short time-scale it appears to be unresponsive to small
   amounts of queuing.  Also, part-way through, a low latency responsive
   flow (r) joins in, aiming to fill the balance of capacity left by the
   other two.

   The combination of the first burst and the low application-limited
   rate of the video causes neither flow to accumulate queuing score.
   In contrast, the second burst causes similar excessive overload
   (125%) to the example in Figure 1.  Then, the video happens to reduce
   its rate (probably due to a less complex scene) so the third burst
   causes only a little congestion.  Let us assume the resulting queue
   causes probNative to rise to just 1%, then the queuing score will
   only accumulate 1% of the size of each packet of flows v and b during
   this burst.

   The fourth burst happens to arrive just as the new responsive flow
   (r) has filled the available capacity, so it leads to very rapid
   growth of the queue.  After a round trip the responsive flow rapidly
   backs off, and the adaptive video also backs off more rapidly than it
   would normally, because of the very high congestion level.  The rapid
   response to congestion of flow r reduces the queuing score that all
   three flows accumulate, but they each still bear the cost in
   proportion to the product of the rates at which their packets arrive
   at the queue and the value of probNative when they do so.  Thus,
   during the fifth burst, they all accumulate less score than the
   fourth, because the queuing delay is not as excessive.

5.2.  Rationale for Aging the Queuing Score

   Even well-behaved flows will not always be able to respond fast
   enough to dynamic events.  Also well-behaved flows, e.g., DCTCP
   [RFC8257], TCP Prague [I-D.briscoe-iccrg-prague-congestion-control],
   BBRv2 [BBRv2] or the L4S variant of SCReAM [SCReAM] for real-time
   media [RFC8298], can maintain a very shallow queue by continual
   careful probing for more while also continually subtracting a little
   from their rate (or congestion window) in response to low levels of
   ECN signalling.  Therefore, the QProt algorithm needs to continually
   offer a degree of forgiveness to age out the queuing score as it
   accumulates.

   Scalable congestion controllers such as those above maintain their
   congestion window in inverse proportion to the congestion level,
   probNative.  That leads to the important property that on average a
   scalable flow holds the product of its congestion window and the
   congestion level constant, no matter the capacity of the link or how



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   many other flows it competes with.  For instance, if the link
   capacity doubles, a scalable flow induces half the congestion
   probability.  Or if three scalable flows compete for the capacity,
   each flow will reduce to one third of the capacity they would use on
   their own and increase the congestion level by 3x.

   This suggests that the QProt algorithm will not sanction a well-
   behaved scalable flow if it ages out the queuing score at a
   sufficient constant rate.  The constant will need to be somewhat
   above the average of a well-behaved scalable flow to allow for normal
   dynamics.

   Relating QProt's aging constant to a scalable flow does not mean that
   a flow has to behave like a scalable flow.  It can be less
   aggressive, but not more.  For instance, a longer RTT flow can run at
   a lower congestion-rate than the aging rate, but it can also increase
   its aggressiveness to equal the rate of short RTT scalable flows
   [ScalingCC].  The constant aging of QProt also means that a long-
   running unresponsive flow will be prone to trigger QProt if it runs
   faster than a competing responsive scalable flow would.  And, of
   course, if a flow causes excessive queuing in the short-term, its
   queuing score will still rise faster than the constant aging process
   will decrease it.  Then QProt will still eject the flow's packets
   before they harm the low latency of the shared queue.

5.3.  Rationale for Transformed Queuing Score

   The QProt algorithm holds a flow's queuing score state in a structure
   called a bucket, because of its similarity to a classic leaky bucket
   (except the contents of the bucket does not represent bytes).

   probNative * pkt_sz   probNative * pkt_sz / AGING
             |                        |
          |  V  |                  |  V  |
          |  :  |        ___       |  :  |
          |_____|        ___       |_____|
          |     |        ___       |     |
          |__ __|                  |__ __|
             |                        |
             V                        V
        AGING * Dt                    Dt

                 Figure 3: Transformation of Queuing Score

   The accumulation and aging of the queuing score is shown on the left
   of Figure 3 in token bucket form.  Dt is the difference between the
   times when the scores of the current and previous packets were
   processed.



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   A transformed equivalent of this token bucket is shown on the right
   of Figure 3, dividing both the input and output by the constant AGING
   rate.  The result is a bucket-depth that represents time and it
   drains at the rate that time passes.

   As a further optimization, the time the bucket was last updated is
   not stored with the flow-state.  Instead, when the bucket is
   initialized the queuing score is added to the system time 'now' and
   the resulting expiry time is written into the bucket.  Subsequently,
   if the bucket has not expired, the incremental queuing score is added
   to the time already held in the bucket.  Then the queuing score
   always represents the expiry time of the flow-state itself.  This
   means that the queuing score does not need to be aged explicitly
   because it ages itself implicitly.

5.4.  Rationale for Policy Conditions

   Pseudocode for the QProt policy conditions is given in Section 4.1
   within the second half of the qprotect() function.  When each packet
   arrives, after finding its flow state and updating the queuing score
   of the packet's flow, the algorithm checks whether the shared queue
   delay exceeds a constant threshold CRITICALqL (e.g., 2 ms), as
   repeated below for convenience:

   <CODE BEGINS>
      if (  ( qdelay > CRITICALqL )  // Test if qdelay over threshold...
         // ...and if flow's q'ing score scaled by qdelay/CRITICALqL
         // ...exceeds CRITICALqLSCORE
         && ( qdelay * qLscore > CRITICALqLPRODUCT ) )
         // Recall that CRITICALqLPRODUCT = CRITICALqL * CRITICALqLSCORE
   <CODE ENDS>

   If the queue delay threshold is exceeded, the flow's queuing score is
   temporarily scaled up by the ratio of the current queue delay to the
   threshold queuing delay, CRITICALqL (the reason for the scaling is
   given next).  If this scaled up score exceeds another constant
   threshold CRITICALqLSCORE, the packet is ejected.  The actual last
   line of code above multiplies both sides of the second condition by
   CRITICALqL to avoid a costly division.

   This approach allows each packet to be assessed once, as it arrives.
   Once queue delay exceeds the threshold, it has two implications:

   *  The current packet might be ejected even though there are packets
      already in the queue from flows with higher queuing scores.
      However, any flow that continues to contribute to the queue will
      have to send further packets, giving an opportunity to eject them
      as well, as they subsequently arrive.



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   *  The next packets to arrive might not be ejected, because they
      might belong to flows with low queuing scores.  In this case,
      queue delay could continue to rise with no opportunity to eject a
      packet.  This is why the queuing score is scaled up by the current
      queue delay.  Then, the more the queue has grown without ejecting
      a packet, the more the algorithm 'raises the bar' to further
      packets.

   The above approach is preferred over the extra per-packet processing
   cost of searching the buckets for the flow with the highest queuing
   score and searching the queue for one of its packets to eject (if one
   is still in the queue).

   Note that by default CRITICALqL_us is set to the maximum threshold of
   the ramp marking algorithm, MAXTH_us.  However, there is some debate
   as to whether setting it to the minimum threshold instead would
   improve QProt performance.  This would roughly double the ratio of
   qdelay to CRITICALqL, which is compared against the CRITICALqLSCORE
   threshold.  So the threshold would have to be roughly doubled
   accordingly.

   Figure 4 explains this approach graphically.  On the horizontal axis
   it shows actual harm, meaning the queuing delay in the shared queue.
   On the vertical axis it shows the behaviour record of the flow
   associated with the currently arriving packet, represented in the
   algorithm by the flow's queuing score.  The shaded region represents
   the combination of actual harm and behaviour record that will lead to
   the packet being ejected.























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   Behaviour Record:
   Queueing Score of
   Arriving Packet's Flow
   ^
   |   +          |/ / / / / / / / / / / / / / / / / / /
   |    +   N     | / / / / / / / / / / / / / / / / / / /
   |     +        |/ / / / /                   / / / / /
   |      +       | / / / /  E (Eject packet)   / / / / /
   |       +      |/ / / / /                   / / / / /
   |         +    | / / / / / / / / / / / / / / / / / / /
   |           +  |/ / / / / / / / / / / / / / / / / / /
   |             +| / / / / / / / / / / / / / / / / / / /
   |              |+ / / / / / / / / / / / / / / / / / /
   |    N         |   + / / / / / / / / / / / / / / / / /
   | (No actual   |       +/ / / / / / / / / / / / / / /
   |   harm)      |            +  / / / / / / / / / / / /
   |              | P (Pass over)   +   ,/ / / / / / / /
   |              |                           ^ + /./ /_/
   +--------------+------------------------------------------>
             CRITICALqL        Actual Harm: Shared Queue Delay

          Figure 4: Graphical Explanation of the Policy Conditions

   The regions labelled 'N' represent cases where the first condition is
   not met - no actual harm - queue delay is below the critical
   threshold, CRITICALqL.

   The region labelled 'E' represents cases where there is actual harm
   (queue delay exceeds CRITICALqL) and the queuing score associated
   with the arriving packet is high enough to be able to eject it with
   certainty.

   The region labelled 'P' represents cases where there is actual harm,
   but the queuing score of the arriving packet is insufficient to eject
   it, so it has to be Passed over.  This adds to queuing delay, but the
   alternative would be to sanction an innocent flow.  It can be seen
   that, as actual harm increases, the judgement of innocence becomes
   increasingly stringent; the behaviour record of the next packet's
   flow does not have to be as bad to eject it.

   Conditioning ejection on actual harm helps prevent VPN packets being
   ejected unnecessarily.  VPNs consisting of multiple flows can tend to
   accumulate queuing score faster than it is aged out, because the
   aging rate is intended for a single flow.  However, whether or not
   some traffic is in a VPN, the queue delay threshold (CRITICALqL) will
   be no more likely to be exceeded.  So conditioning ejection on actual
   harm helps reduce the chance that VPN traffic will be ejected by the
   QProt function.



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5.5.  Rationale for Reclassification as the Policy Action

   When the DOCSIS QProt algorithm deems that it is necessary to eject a
   packet to protect the Low Latency queue, it redirects the packet to
   the Classic queue.  In the Low Latency DOCSIS architecture (as in
   Coupled DualQ AQMs generally), the Classic queue is expected to
   frequently have a larger backlog of packets, caused by classic
   congestion controllers interacting with a classic AQM (which has a
   latency target of 10ms) as well as other bursty traffic.

   Therefore, typically, an ejected packet will experience higher
   queuing delay than it would otherwise, and it could be re-ordered
   within its flow (assuming QProt does not eject all packets of an
   anomalous flow).  The mild harm caused to the performance of the
   ejected packet's flow is deliberate.  It gives senders a slight
   incentive to identify their packets correctly.

   If there were no such harm, there would be nothing to prevent all
   flows from identifying themselves as suitable for classification into
   the low latency queue, and just letting QProt sort the resulting
   aggregate into queue-building and non-queue-building flows.  This
   might seem like a useful alternative to requiring senders to
   correctly identify their flows.  However, handling of mis-classified
   flows is not without a cost.  The more packets that have to be
   reclassified, the more often the delay of the low latency queue would
   exceed the threshold.  Also more memory would be required to hold the
   extra flow state.

   When a packet is redirected into the Classic queue, an operator might
   want to alter the identifier(s) that originally caused it to be
   classified into the Low Latency queue, so that the packet will not be
   classified into another low latency queue further downstream.
   However, redirection of occasional packets can be due to unusually
   high transient load just at the specific bottleneck, not necessarily
   at any other bottleneck, and not necessarily due to bad flow
   behaviour.  Therefore, Section 5.4.1.2 of [I-D.ietf-tsvwg-ecn-l4s-id]
   precludes a network node from altering the end-to-end ECN field to
   exclude traffic from L4S treatment.  Instead a local-use identifier
   ought to be used (e.g., Diffserv Codepoint or VLAN tag), so that each
   operator can apply its own policy, without prejudging what other
   operators ought to do.

   Although not supported in the DOCSIS specs, QProt could be extended
   to recognize that large numbers of redirected packets belong to the
   same flow.  This might be detected when the bucket expiry time t_exp
   exceeds a threshold.  Depending on policy and implementation
   capabilities, QProt could then install a classifier to redirect a
   whole flow into the Classic queue, with an idle timeout to remove



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   stale classifiers.  In these 'persistent offender' cases, QProt might
   also overwrite each redirected packet's DSCP or clear its ECN field
   to Not-ECT, in order to protect other potential L4S queues
   downstream.  The DOCSIS specs do not discuss sanctioning whole flows,
   so further discussion is beyond the scope of the present document.

6.  Limitations

   The QProt algorithm groups packets with common layer-4 flow
   identifiers.  It then uses this grouping to accumulate queuing scores
   and to sanction packets.

   This choice of identifier for grouping is pragmatic with no
   scientific basis.  All the packets of a flow certainly pass between
   the same two endpoints.  But some applications might initiate
   multiple flows between the same end-points, e.g., for media, control,
   data, etc.  Others might use common flow identifiers for all these
   streams.  Also, a user might group multiple application flows within
   the same encrypted VPN between the same layer-4 tunnel end-points.
   And even if there were a one-to-one mapping between flows and
   applications, there is no reason to believe that the rate at which
   congestion can be caused ought to be allocated on a per application
   flow basis.

   The use of a queuing score that excludes those aspects of flow rate
   that do not contribute to queuing (Section 5.1) goes some way to
   mitigating this limitation, because the algorithm does not judge
   responsibility for queuing delay primarily on the combined rate of a
   set of flows grouped under one flow ID.

7.  IANA Considerations (to be removed by RFC Editor)

   This specification contains no IANA considerations.

8.  Implementation Status

    +================+================================================+
    | Implementation | DOCSIS models for ns-3                         |
    | name:          |                                                |
    +================+================================================+
    | Organization   | CableLabs                                      |
    +----------------+------------------------------------------------+
    | Web page       | https://apps.nsnam.org/app/docsis-ns3/         |
    +----------------+------------------------------------------------+
    | Description    | ns-3 simulation models developed and used in   |
    |                | support of the Low Latency DOCSIS development, |
    |                | including models of Dual Queue Coupled AQM,    |
    |                | Queue Protection, and the DOCSIS MAC           |



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    +----------------+------------------------------------------------+
    | Maturity       | Simulation models that can also be used in     |
    |                | emulation mode in a testbed context            |
    +----------------+------------------------------------------------+
    | Coverage       | Complete implementation of Annex P of DOCSIS   |
    |                | 3.1                                            |
    +----------------+------------------------------------------------+
    | Version        | DOCSIS 3.1, version I21;                       |
    |                | https://www.cablelabs.com/specifications/CM-   |
    |                | SP-MULPIv3.1?v=I21                             |
    +----------------+------------------------------------------------+
    | Licence        | GPLv2                                          |
    +----------------+------------------------------------------------+
    | Contact        | via web page                                   |
    +----------------+------------------------------------------------+
    | Last Impl'n    | Mar 2022                                       |
    | update         |                                                |
    +----------------+------------------------------------------------+
    | Information    | 7 Mar 2022                                     |
    | valid at       |                                                |
    +----------------+------------------------------------------------+

                                  Table 1

   There are also a number of closed source implementations, including 2
   cable modem implementations written by different chipset
   manufacturers, and one CMTS implementation by a third manufacturer.
   These, as well as the ns-3 implementation, have passed the full suite
   of compliance tests developed by CableLabs.

9.  Security Considerations

   The whole of this document concerns traffic security.  It considers
   the security question of how to identify and eject traffic that does
   not comply with the non-queue-building behaviour required to use a
   shared low latency queue, whether accidentally or maliciously.

   Section 8.2 of the L4S architecture [I-D.ietf-tsvwg-l4s-arch]
   introduces the problem of maintaining low latency by either self-
   restraint or enforcement, and places DOCSIS queue protection in
   context within a wider set of approaches to the problem.










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9.1.  Resource Exhaustion Attacks

   The algorithm has been designed to fail gracefully in the face of
   traffic crafted to overrun the resources used for the algorithm's own
   processing and flow state.  This means that non-queue-building flows
   will always be less likely to be sanctioned than queue-building
   flows.  But an attack could be contrived to deplete resources in such
   a way that the proportion of innocent (non-queue-building) flows that
   are incorrectly sanctioned could increase.

   Incorrect sanctioning is intended not to be catastrophic; it results
   in more packets from well-behaved flows being redirected into the
   Classic queue; thus introducing more reordering into innocent flows.

9.1.1.  Exhausting Flow-State Storage

   To exhaust the number of buckets, the most efficient attack is to
   send enough long-running attack flows to increase the chance that an
   arriving flow will not find an available bucket, and therefore have
   to share the 'dregs' bucket.  For instance, if ATTEMPTS=2 and
   NBUCKETS=32, it requires about 94 attack flows, each using different
   port numbers, to increase the probability to 99% that an arriving
   flow will have to share the dregs, where it will share a high degree
   of redirection into the C queue with the remainder of the attack
   flows.

   For an attacker to keep buckets busy, it is more efficient to hold
   onto them by cycling regularly through a set of port numbers (94 in
   the above example), rather than to keep occupying and releasing
   buckets with single packet flows across a much larger number of
   ports.

   During such an attack, the coupled marking probability will have
   saturated at 100%. So, to hold a bucket, the rate of an attack flow
   needs to be no less than the AGING rate of each bucket; 4Mb/s by
   default.  However, for an attack flow to be sure to hold on to its
   bucket, it would need to send somewhat faster.  Thus an attack with
   100 flows would need a total force of more than 100 * 4Mb/s = 400Mb/
   s.

   This attack can be mitigated (but not prevented) by increasing the
   number of buckets.  The required attack force scales linearly with
   the number of buckets, NBUCKETS.  So, if NBUCKETS were doubled to 64,
   twice as many 4Mb/s flows would be needed to maintain the same impact
   on innocent flows.






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   Probably the most effective mitigation would be to implement
   redirection of whole-flows once enough of the individual packets of a
   certain offending flow had been redirected.  This would free up the
   buckets used to maintain the per-packet queuing score of persistent
   offenders.  Whole-flow redirection is outside the scope of the
   current version of the QProt algorithm specified here, but it is
   briefly discussed at the end of Section 5.5.

   It might be considered that all the packets of persistently offending
   flows ought to be discarded rather than redirected.  However, this is
   not recommended, because attack flows might be able to pervert whole-
   flow discard, turning it onto at least some innocent flows, thus
   amplifying an attack that causes reordering into total deletion of
   some innocent flows.

9.1.2.  Exhausting Processing Resources

   The processing time needed to apply the QProt algorithm to each L
   packet is small and intended not to take all the time available
   between each of a run of fairly small packets.  However, an attack
   could use minimum size packets launched from multiple input
   interfaces into a lower capacity output interface.  Whether the QProt
   algorithm is vulnerable to processor exhaustion will depend on the
   specific implementation.

   Addition of a capability to redirect persistently offending flows
   from L to C would be the most effective way to reduce the per-packet
   processing cost of the QProt algorithm, when under attack.  As
   already mentioned in Section 9.1.1, this would also be an effective
   way to mitigate flow-state exhaustion attacks.  Further discussion of
   whole-flow redirection is outside the scope of the present document,
   but briefly discussed at the end of Section 5.5.

10.  Comments Solicited

   Evaluation and assessment of the algorithm by researchers is
   solicited.  Comments and questions are also encouraged and welcome.
   They can be addressed to the authors.

11.  Acknowledgements

   Thanks to Tom Henderson, Magnus Westerlund, David Black, Adrian
   Farrel and Gorry Fairhurst for their reviews of this document.  The
   design of the QProt algorithm and the settings of the parameters
   benefited from discussion and critique from the participants of the
   cable industry working group on Low Latency DOCSIS.  CableLabs funded
   Bob Briscoe's initial work on this document.




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12.  References

12.1.  Normative References

   [DOCSIS]   CableLabs, "MAC and Upper Layer Protocols Interface
              (MULPI) Specification, CM-SP-MULPIv3.1", Data-Over-Cable
              Service Interface Specifications DOCSIS® 3.1 Version I17
              or later, 21 January 2019, <https://specification-
              search.cablelabs.com/CM-SP-MULPIv3.1>.

   [DOCSIS-CCAP-OSS]
              CableLabs, "CCAP Operations Support System Interface
              Spec", Data-Over-Cable Service Interface Specifications
              DOCSIS® 3.1 Version I14 or later, 21 January 2019,
              <https://specification-search.cablelabs.com/CM-SP-CM-
              OSSIv3.1>.

   [DOCSIS-CM-OSS]
              CableLabs, "Cable Modem Operations Support System
              Interface Spec", Data-Over-Cable Service Interface
              Specifications DOCSIS® 3.1 Version I14 or later, 21
              January 2019, <https://specification-search.cablelabs.com/
              CM-SP-CM-OSSIv3.1>.

   [I-D.ietf-tsvwg-ecn-l4s-id]
              Schepper, K. D. and B. Briscoe, "Explicit Congestion
              Notification (ECN) Protocol for Very Low Queuing Delay
              (L4S)", Work in Progress, Internet-Draft, draft-ietf-
              tsvwg-ecn-l4s-id-25, 4 March 2022,
              <https://datatracker.ietf.org/doc/html/draft-ietf-tsvwg-
              ecn-l4s-id-25>.

   [I-D.ietf-tsvwg-nqb]
              White, G. and T. Fossati, "A Non-Queue-Building Per-Hop
              Behavior (NQB PHB) for Differentiated Services", Work in
              Progress, Internet-Draft, draft-ietf-tsvwg-nqb-10, 4 March
              2022, <https://datatracker.ietf.org/doc/html/draft-ietf-
              tsvwg-nqb-10>.

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP",
              RFC 3168, DOI 10.17487/RFC3168, September 2001,
              <https://www.rfc-editor.org/info/rfc3168>.

   [RFC8311]  Black, D., "Relaxing Restrictions on Explicit Congestion
              Notification (ECN) Experimentation", RFC 8311,
              DOI 10.17487/RFC8311, January 2018,
              <https://www.rfc-editor.org/info/rfc8311>.



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12.2.  Informative References

   [BBRv2]    Cardwell, N., "TCP BBR v2 Alpha/Preview Release", github
              repository; Linux congestion control module,
              <https://github.com/google/bbr/blob/v2alpha/README.md>.

   [I-D.briscoe-iccrg-prague-congestion-control]
              Schepper, K. D., Tilmans, O., and B. Briscoe, "Prague
              Congestion Control", Work in Progress, Internet-Draft,
              draft-briscoe-iccrg-prague-congestion-control-00, 9 March
              2021, <https://datatracker.ietf.org/doc/html/draft-
              briscoe-iccrg-prague-congestion-control-00>.

   [I-D.ietf-tsvwg-aqm-dualq-coupled]
              Schepper, K. D., Briscoe, B., and G. White, "DualQ Coupled
              AQMs for Low Latency, Low Loss and Scalable Throughput
              (L4S)", Work in Progress, Internet-Draft, draft-ietf-
              tsvwg-aqm-dualq-coupled-23, 4 May 2022,
              <https://datatracker.ietf.org/doc/html/draft-ietf-tsvwg-
              aqm-dualq-coupled-23>.

   [I-D.ietf-tsvwg-l4s-arch]
              Briscoe, B., Schepper, K. D., Bagnulo, M., and G. White,
              "Low Latency, Low Loss, Scalable Throughput (L4S) Internet
              Service: Architecture", Work in Progress, Internet-Draft,
              draft-ietf-tsvwg-l4s-arch-17, 4 March 2022,
              <https://datatracker.ietf.org/doc/html/draft-ietf-tsvwg-
              l4s-arch-17>.

   [LLD]      White, G., Sundaresan, K., and B. Briscoe, "Low Latency
              DOCSIS: Technology Overview", CableLabs White Paper ,
              February 2019, <https://cablela.bs/low-latency-docsis-
              technology-overview-february-2019>.

   [RFC4303]  Kent, S., "IP Encapsulating Security Payload (ESP)",
              RFC 4303, DOI 10.17487/RFC4303, December 2005,
              <https://www.rfc-editor.org/info/rfc4303>.

   [RFC6789]  Briscoe, B., Ed., Woundy, R., Ed., and A. Cooper, Ed.,
              "Congestion Exposure (ConEx) Concepts and Use Cases",
              RFC 6789, DOI 10.17487/RFC6789, December 2012,
              <https://www.rfc-editor.org/info/rfc6789>.

   [RFC7713]  Mathis, M. and B. Briscoe, "Congestion Exposure (ConEx)
              Concepts, Abstract Mechanism, and Requirements", RFC 7713,
              DOI 10.17487/RFC7713, December 2015,
              <https://www.rfc-editor.org/info/rfc7713>.




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   [RFC8257]  Bensley, S., Thaler, D., Balasubramanian, P., Eggert, L.,
              and G. Judd, "Data Center TCP (DCTCP): TCP Congestion
              Control for Data Centers", RFC 8257, DOI 10.17487/RFC8257,
              October 2017, <https://www.rfc-editor.org/info/rfc8257>.

   [RFC8298]  Johansson, I. and Z. Sarker, "Self-Clocked Rate Adaptation
              for Multimedia", RFC 8298, DOI 10.17487/RFC8298, December
              2017, <https://www.rfc-editor.org/info/rfc8298>.

   [ScalingCC]
              Briscoe, B. and K. De Schepper, "Resolving Tensions
              between Congestion Control Scaling Requirements", Simula
              Technical Report TR-CS-2016-001 arXiv:1904.07605, July
              2017, <https://arxiv.org/abs/1904.07605>.

   [SCReAM]   Johansson, I., "SCReAM", github repository; ,
              <https://github.com/EricssonResearch/scream/blob/master/
              README.md>.

Authors' Addresses

   Bob Briscoe (editor)
   Independent
   United Kingdom
   Email: ietf@bobbriscoe.net
   URI:   http://bobbriscoe.net/


   Greg White
   CableLabs
   United States of America
   Email: G.White@CableLabs.com



















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