Internet DRAFT - draft-briscoe-docsis-q-protection
draft-briscoe-docsis-q-protection
Network Working Group B. Briscoe, Ed.
Internet-Draft Independent
Intended status: Informational G. White
Expires: 26 May 2024 CableLabs
23 November 2023
The DOCSIS® Queue Protection Algorithm to Preserve Low Latency
draft-briscoe-docsis-q-protection-07
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
Task Force (IETF). Note that other groups may also distribute
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This Internet-Draft will expire on 26 May 2024.
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Copyright Notice
Copyright (c) 2023 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 Constant Aging of 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 . . . . . . . . . . . . . . . . . 30
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], [RFC9331]). 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 [RFC9332]. 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 incentive
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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], [RFC9331]. 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 [RFC9331]
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 or 'LL' queue (the 'Native' AQM of the LL queue is
defined in the Immediate Active Queue Management Annex of [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; // LL 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 LL AQM 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 ECN-marking probability of the
native LL AQM
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 at the LL queue, qprotect() measures the
current delay of the LL queue and derives the native LL 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 of the LL queue 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 Constant Aging of 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],
BBRv3 [BBRv3] 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. Therefore, in
steady state, a scalable flow will induce the same constant amount of
"congested-bytes" per round trip, whatever the link capacity, and no
matter how many flows are sharing the capacity.
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
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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.
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:
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* 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.
* 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 [RFC9331] 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 [RFC9330] 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 LL
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 LL 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-nqb]
White, G., Fossati, T., and R. Geib, "A Non-Queue-Building
Per-Hop Behavior (NQB PHB) for Differentiated Services",
Work in Progress, Internet-Draft, draft-ietf-tsvwg-nqb-21,
7 November 2023, <https://datatracker.ietf.org/doc/html/
draft-ietf-tsvwg-nqb-21>.
[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>.
[RFC9331] De Schepper, K. and B. Briscoe, Ed., "The Explicit
Congestion Notification (ECN) Protocol for Low Latency,
Low Loss, and Scalable Throughput (L4S)", RFC 9331,
DOI 10.17487/RFC9331, January 2023,
<https://www.rfc-editor.org/info/rfc9331>.
12.2. Informative References
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[BBRv3] Cardwell, N., "TCP BBR v3 Release", github
repository; Linux congestion control module,
<https://github.com/google/bbr/blob/v3/README.md>.
[I-D.briscoe-iccrg-prague-congestion-control]
De Schepper, K., Tilmans, O., Briscoe, B., and V. Goel,
"Prague Congestion Control", Work in Progress, Internet-
Draft, draft-briscoe-iccrg-prague-congestion-control-03,
14 October 2023, <https://datatracker.ietf.org/doc/html/
draft-briscoe-iccrg-prague-congestion-control-03>.
[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>.
[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>.
[RFC9330] Briscoe, B., Ed., De Schepper, K., Bagnulo, M., and G.
White, "Low Latency, Low Loss, and Scalable Throughput
(L4S) Internet Service: Architecture", RFC 9330,
DOI 10.17487/RFC9330, January 2023,
<https://www.rfc-editor.org/info/rfc9330>.
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[RFC9332] De Schepper, K., Briscoe, B., Ed., and G. White, "Dual-
Queue Coupled Active Queue Management (AQM) for Low
Latency, Low Loss, and Scalable Throughput (L4S)",
RFC 9332, DOI 10.17487/RFC9332, January 2023,
<https://www.rfc-editor.org/info/rfc9332>.
[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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