Identifying and Handling Non Queue Building Flows in a Bottleneck Link
draft-white-tsvwg-nqb-00
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| Author | Greg White | ||
| Last updated | 2018-10-22 | ||
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draft-white-tsvwg-nqb-00
Transport Area Working Group G. White
Internet-Draft CableLabs
Intended status: Informational October 22, 2018
Expires: April 25, 2019
Identifying and Handling Non Queue Building Flows in a Bottleneck Link
draft-white-tsvwg-nqb-00
Abstract
This draft discusses the potential to improve quality of experience
for broadband internet applications by distinguishing between flows
that cause queuing latency and flows that don't.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on April 25, 2019.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Non-Queue Building Flows . . . . . . . . . . . . . . . . . . 3
3. Identifying NQB traffic . . . . . . . . . . . . . . . . . . . 3
3.1. Endpoint marking . . . . . . . . . . . . . . . . . . . . 4
3.2. Queuing behavior analysis . . . . . . . . . . . . . . . . 5
4. Non Queue Building PHB . . . . . . . . . . . . . . . . . . . 5
5. End-to-end Support . . . . . . . . . . . . . . . . . . . . . 6
6. Relationship to L4S . . . . . . . . . . . . . . . . . . . . . 6
7. Comparison to Existing Approaches . . . . . . . . . . . . . . 6
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 7
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 7
10. Security Considerations . . . . . . . . . . . . . . . . . . . 7
11. Informative References . . . . . . . . . . . . . . . . . . . 7
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 8
1. Introduction
Residential broadband internet services are commonly configured with
a single bottleneck link (the access network link) upon which the
service definition is applied. The service definition, typically an
upstream/downstream data rate tuple, is implemented as a configured
pair of rate shapers that are applied to the user's traffic. In such
networks, the quality of service that each application receives, and
as a result, the quality of experience that it generates for the user
is influenced by the characteristics of the access network link.
The vast majority of packets that are carried by residential
broadband access networks are managed by an end-to-end congestion
control algorithm, such as Reno, Cubic or BBR. These congestion
control algorithms attempt to seek the available capacity of the end-
to-end path (which in the case of residential broadband networks, can
frequently be the access network link), and in doing so generally
overshoot the available capacity, causing a queue to build-up at the
bottleneck link. This queue build up results in queuing delay that
the application experiences as variable latency.
In contrast to congestion-controlled applications, there are a
variety of relatively low data rate applications that do not
materially contribute to queueing delay, but are nonetheless
subjected to it by sharing the same bottleneck link in the access
network. Many of these applications may be sensitive to latency or
latency variation, and thus produce a poor quality of experience in
such conditions.
Active Queue Management (AQM) mechanisms (such as PIE [RFC8033],
DOCSIS-PIE [RFC8034], or CoDel [RFC8289]) can improve the quality of
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experience for latency sensitive applications, but there are
practical limits to the amount of improvement that can be achieved
without impacting the throughput of capacity-seeking applications.
This document considers differentiating between these two classes of
traffic in bottleneck links in order that both classes can deliver
exceptional quality of experience for their applications, and
solicits discussion / feedback.
2. Non-Queue Building Flows
There are many applications that send traffic at relatively low data
rates and/or in a fairly smooth and consistent manner such that they
are highly unlikely to exceed the available capacity of the network
path between source and sink. Such applications are ideal candidates
to be queued separately from the capacity-seeking applications that
cause queue buildups and latency.
These Non-queue-building (NQB) flows are typically UDP flows, which
send traffic at a lower data rate and don't seek the capacity of the
link (examples: online games, voice chat, dns lookups). Here the
data rate is essentially limited by the Application itself. In
contrast, Queue-building (QB) flows include traffic which uses the
Traditional TCP, QUIC, BBR or other TCP variants.
There are a lot of great examples of applications that fall very
neatly into these two categories, but there are also application
flows that may be in a gray area in between (e.g. they are NQB on
high-speed links, but QB on slow-speed links).
3. Identifying NQB traffic
This memo is intended to seek feedback on mechanisms by which Non-
Queue Building flows can be identified by the network in an
application-neutral way. Two mechanisms in particular seem feasible,
and could (either alone or in concert) be used to differentiate
between QB and NQB flows.
o Endpoint marking. This mechanism would have application endpoints
apply a marking (perhaps utilizing the Diffserv field of the IP
header) to NQB flows that could then be used by the network to
differentiate between QB and NQB flows.
o Queuing behavior analysis. This mechanism would utilize real-time
per-flow traffic statistics to identify whether a flow is sending
traffic at a rate that exceeds the available capacity of the
bottleneck link and hence is causing a queue to form.
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3.1. Endpoint marking
This mechanism would have application endpoints apply a marking
(perhaps utilizing the Diffserv field of the IP header) to NQB flows
that could then be used by the network to differentiate between QB
and NQB flows. It would be useful for such a marking to be
universally agreed upon, rather than being locally defined by the
network operator, such that applications could be written to apply
the marking without regard to local network policies.
Some questions that arise when considering endpoint marking are: How
can an application determine whether it is queue building or not,
given that the sending application is generally not aware of the
available capacity of the path to the receiving endpoint? Even in
cases where an application is aware of the capacity of the path, how
can it be sure that the available capacity (considering other flows
that may be sharing the path) would be sufficient to result in the
application's traffic not causing a queue to form? In an unmanaged
environment, how can networks trust endpoint marking, why wouldn't
all applications mark their packets as NQB?
As an answer the last question, it would be worthwhile to note that
the NQB designation and marking would be intended to convey
verifiable traffic behavior, not needs or wants. Also, it would be
important that incentives are aligned correctly, i.e. that there is a
benefit to the application in marking its packets correctly, and no
benefit for an application in intentionally mismarking its traffic.
Thus, a useful property of nodes that support separate queues for NQB
and QB flows would be that for NQB flows, the NQB queue provides
better performance (considering latency, loss and throughput) than
the QB queue; and for QB flows, the QB queue provides better
performance (considering latency, loss and throughput) than the NQB
queue.
Even so, it is possible that due to an implementation error or
misconfiguration, a QB flow would end up getting mismarked as NQB, or
vice versa. In the case of an NQB flow that isn't marked as NQB and
ends up in the QB queue, it would only impact its own quality of
service, and so it seems to be of lesser concern. However, a QB flow
that is mismarked as NQB, either due to error or due to the fact that
the application developer can't predict the data rate capabilities of
the link, would causing queuing delays for all of the other flows
that are sharing the NQB queue.
To prevent this situation from harming the performance of the real
NQB flows, it would likely be valuable to support a "queue
protection" function that could identify QB flows that are mismarked
as NQB, and reclassify those flows/packets to the QB queue. This
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would benefit the reclassified flow by giving it access to a large
buffer (and thus lower packet loss rate), and would benefit the
actual NQB flows by preventing harm (increased latency variability)
to them. Some open questions around this function include: How could
such a function be implemented in an objective and verifiable manner?
What other options might exist to serve this purpose in a dual-queue
architecture?
3.2. Queuing behavior analysis
Similar to the queue protection function outlined in the previous
section, it may be feasible to devise a real time flow analyzer for a
node that would identify flows that are causing queue build up, and
redirect those flows to the QB queue, leaving the remaining flows in
the NQB queue.
4. Non Queue Building PHB
This section uses the DiffServ nomenclature of per-hop-behavior (PHB)
to describe how a network node could provide better quality of
service for NQB flows without reducing performance of QB flows.
A node supporting the NQB PHB would provide a separate queue for non-
queue-building traffic. This queue would support a latency-based
queue protection mechanism that is able to identify queue-building
behavior in flows that are classified into the queue, and to redirect
flows causing queue build up to a different queue.
While there may be some similarities between the characteristics of
NQB flows and flows marked with the Expedited Forwarding DSCP, the
NQB PHB would differ from the Expedited Forwarding PHB in several
important ways.
o NQB traffic is not rate limited or rate policed. Rather, the NQB
queue would be expected to support a latency-based queue
protection mechanism that identifies NQB marked flows that are
beginning to cause latency, and redirects packets from those flows
to the queue for QB flows.
o The node supporting the NQB PHB makes no guarantees on latency or
data rate for NQB marked flows, but instead aims to provide sub-
millisecond queuing delays for as many such marked flows as it
can, and shed load when needed.
o EF is commonly used exclusively for voice traffic, for which
additional functions are applied, such as admission control,
accounting, prioritized delivery, etc.
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In networks that support the NQB PHB, it may be preferred to also
include traffic marked EF (101110b) in the NQB queue. The choice of
the 0x2A codepoint (101010b) for NQB would conveniently allow a node
to select these two codepoints using a single mask pattern of
101x10b.
5. End-to-end Support
In contrast to the existing standard DSCPs, which are typically only
enforced within a DiffServ Domain (e.g. an AS), this DSCP would be
intended for end-to-end usage across the Internet. Some access
network service providers bleach the Diffserv field on ingress into
their network, and in some cases apply their own DSCP for internal
usage. Access networks that support the NQB PHB would need to permit
the NQB PHB to pass through this bleaching operation such that the
PHB can be provided at the access network link.
6. Relationship to L4S
The dual-queue mechanism described in this draft is similar to, and
is intended to be compatible with [I-D.ietf-tsvwg-l4s-arch].
7. Comparison to Existing Approaches
Traditional QoS mechanisms focus on prioritization in an attempt to
achieve two goals, reduced latency for "latency-sensitive" traffic,
and increased bandwidth availability for "important" applications.
Applications are generally given priority in proportion to some
combination of latency-sensitivity and importance.
Downsides to this approach include the difficulties in sorting out
what priority level each application should get (making the value
judgement as to latency-sensitivity and importance), associating
packets to priority levels (lots of classifier state, or trusting
endpoint markings and the value judgements that they convey),
ensuring that high priority traffic doesn't starve lower priority
traffic (admission control, weighted scheduling, etc. are possible
solutions). This solution can work in a managed network, where the
network operator can control the usage of the QoS mechanisms, but has
not been adopted end-to-end across the internet.
Flow queueing approaches (such as fq_codel RFC 8290 [RFC8290]), on
the other hand, achieve latency improvements by associating packets
into "flow" queues and then prioritizing "sparse flows", i.e. packets
that arrive to an empty flow queue. Flow queueing does not attempt
to differentiate between flows on the basis of value (importance or
latency-sensitivity), it simply gives preference to sparse flows, and
tries to guarantee that the non-sparse flows all get an equal share
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of the remaining channel capacity. As a result, fq mechanisms could
be considered more appropriate for unmanaged environments and general
internet traffic.
Downsides to this approach include loss of low latency performance
due to hash collisions (where a sparse flow shares a queue with a
bulk data flow), complexity in managing a large number of queues, and
the scheduling (typically DRR) that enforces that each non-sparse
flow gets an equal fraction of link bandwidth causes problems with
VPNs and other tunnels, exhibits poor behavior with less-aggressive
CA algos, e.g. LEDBAT, and exhibits poor behavior with RMCAT CA
algos. In effect the network element is making a decision as to what
constitutes a flow, and then forcing all such flows to take equal
bandwidth at every instant.
The Dual-queue approach achieves the main benefit of fq_codel:
latency improvement without value judgements, without the downsides.
The distinction between NQB flows and QB flows is similar to the
distinction made between "sparse flow queues" and "non-sparse flow
queues" in fq_codel. In fq_codel, a flow queue is considered sparse
if it is drained completely by each packet transmission, and remains
empty for at least one cycle of the round robin over the active flows
(this is approximately equivalent to saying that it utilizes less
than its fair share of capacity). While this definition is
convenient to implement in fq_codel, it isn't the only useful
definition of sparse flows.
8. Acknowledgements
TBD
9. IANA Considerations
TBD
10. Security Considerations
TBD
11. Informative References
[I-D.ietf-tsvwg-l4s-arch]
Briscoe, B., Schepper, K., and M. Bagnulo, "Low Latency,
Low Loss, Scalable Throughput (L4S) Internet Service:
Architecture", draft-ietf-tsvwg-l4s-arch-02 (work in
progress), March 2018.
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[RFC8033] Pan, R., Natarajan, P., Baker, F., and G. White,
"Proportional Integral Controller Enhanced (PIE): A
Lightweight Control Scheme to Address the Bufferbloat
Problem", RFC 8033, DOI 10.17487/RFC8033, February 2017,
<https://www.rfc-editor.org/info/rfc8033>.
[RFC8034] White, G. and R. Pan, "Active Queue Management (AQM) Based
on Proportional Integral Controller Enhanced PIE) for
Data-Over-Cable Service Interface Specifications (DOCSIS)
Cable Modems", RFC 8034, DOI 10.17487/RFC8034, February
2017, <https://www.rfc-editor.org/info/rfc8034>.
[RFC8289] Nichols, K., Jacobson, V., McGregor, A., Ed., and J.
Iyengar, Ed., "Controlled Delay Active Queue Management",
RFC 8289, DOI 10.17487/RFC8289, January 2018,
<https://www.rfc-editor.org/info/rfc8289>.
[RFC8290] Hoeiland-Joergensen, T., McKenney, P., Taht, D., Gettys,
J., and E. Dumazet, "The Flow Queue CoDel Packet Scheduler
and Active Queue Management Algorithm", RFC 8290,
DOI 10.17487/RFC8290, January 2018,
<https://www.rfc-editor.org/info/rfc8290>.
Author's Address
Greg White
CableLabs
858 Coal Creek Circle
Louisville, CO 80027
US
Email: g.white@cablelabs.com
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