Internet DRAFT - draft-joung-detnet-taxonomy-dataplane
draft-joung-detnet-taxonomy-dataplane
DetNet Working Group J. Joung
Internet-Draft Sangmyung University
Intended status: Informational X. Geng
Expires: 28 August 2024 Huawei
S. Peng
ZTE Corporation
T. Eckert
Futurewei Technologies
25 February 2024
Dataplane Enhancement Taxonomy
draft-joung-detnet-taxonomy-dataplane-01
Abstract
This draft is to facilitate the understanding of the data plane
enhancement solutions, which are suggested currently or can be
suggested in the future, for deterministic networking. This draft
provides criteria for classifying data plane solutions. Examples of
each category are listed, along with reasons where necessary.
Strengths and limitations of the categories are described.
Suitability of the solutions for various services of deterministic
networking are also briefly mentioned.
Status of This Memo
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This Internet-Draft will expire on 28 August 2024.
Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Terms Used in This Document . . . . . . . . . . . . . . . 4
2.2. Abbreviations . . . . . . . . . . . . . . . . . . . . . . 4
3. Conventions Used in This Document . . . . . . . . . . . . . . 4
4. Taxonomy with Performance . . . . . . . . . . . . . . . . . . 4
4.1. Per Hop Dominant Factor for Latency Bound . . . . . . . . 4
5. Taxonomy with Functional Characteristics . . . . . . . . . . 6
5.1. Periodicity . . . . . . . . . . . . . . . . . . . . . . . 6
5.2. Network Synchronization . . . . . . . . . . . . . . . . . 6
5.3. Traffic Granularity . . . . . . . . . . . . . . . . . . . 7
5.4. Work Conserving . . . . . . . . . . . . . . . . . . . . . 9
5.5. Target Transmission Time . . . . . . . . . . . . . . . . 9
5.6. Service Order . . . . . . . . . . . . . . . . . . . . . . 10
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
7. Security Considerations . . . . . . . . . . . . . . . . . . . 12
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 12
9. Contributor . . . . . . . . . . . . . . . . . . . . . . . . . 12
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
10.1. Normative References . . . . . . . . . . . . . . . . . . 12
10.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
1. Introduction
This draft is to facilitate the understanding of the data plane
enhancement solutions, which are suggested currently or can be
suggested in the future, for deterministic networking.
An enhancement solution can be a combination of multiple data plane
functional entities, such as regulators, queues, and schedulers. A
solution can also include functional entities across network nodes,
e.g. traffic enforcement or regulation functions at the edge. A
regulator, or equivalently a shaper, is defined as a functional
entity that makes the arrival process of a flow conform to a
predefined process. A packet scheduler, or simply a scheduler, is a
functional entity that determines when a packet is transmitted.
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We use the term taxonomy as a synonym to the criteria for classifying
the solutions accordingly. A category is a subset of solutions
classified into a single group with a taxonomy. This draft provides
several taxonomies and the criteria for classifying data plane
solutions. These taxonomies are orthogonal to each other.
Examples of the categories are listed, along with reasons where
necessary. Strengths and limitations of the categories are
described.
Suitability of the solutions for various services of deterministic
networking are also briefly mentioned. The services can be
classified according to the flow characteristics and the performance
requirements. For example, Requirements for Reliable Wireless
Industrial Services [I-D.ietf-detnet-raw-industrial-req]
characterizes the services by the latency bound, the burst size, the
burst transmission period, the number of nodes, etc. This document
adopts this characterization rule, and classifies the services into
one of tight/loose latency, large/small burst, periodic/non-periodic,
and large/small scale services. For example, the display information
service defined in Section 4.4. of
[I-D.ietf-detnet-raw-industrial-req] is a loose latency, large burst,
non-periodic, and small scale service.
The taxonomies described in this draft can be applied for the
solutions of other standardization bodies, such as IEEE 802.1 TSN TG.
In this draft, the candidate solutions currently being proposed in
DetNet WG are simply listed without any descriptions. The details of
the solutions are intentionally omitted. Interesting readers may
refer to the corresponding drafts. When necessary, the solutions
from IEEE TSN TG or existing popular ones are used as examples to
better understand the taxonomy and the derived categories.
The mechanisms raised in the DetNet WG are not entirely new concepts
but rather variations of existing mechanisms. These deliberate
approaches aim to address the scalability requirements defined in
[I-D.ietf-detnet-scaling-requirements] while ensuring a degree of
continuity and compatibility with the current practices. The
taxonomy in this draft reflects how new mechanisms extend existing
ones to address scalability challenges.
For instance, Cycle Specified Queuing and Forwarding (CSQF)
[I-D.chen-detnet-sr-based-bounded-latency], Tagged Cyclic Queuing and
Forwarding (TCQF) [I-D.eckert-detnet-tcqf], IEEE 802.1Qdv Enhanced
CQF (ECQF) are enhancements built upon the foundation of Cyclic
Queuing and Forwarding (CQF). Similarly, Work Conserving Stateless
Core Fair Queuing (C-SCORE) [I-D.joung-detnet-stateless-fair-queuing]
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is an extension of Fair Queuing (FQ). Timeslot Queuing and
Forwarding (TQF) [I-D.peng-detnet-packet-timeslot-mechanism] is an
extension of IEEE 802.1Qbv, also known as Time Aware Shaper (TAS).
Earliest Deadline First (EDF)
[I-D.peng-detnet-deadline-based-forwarding] proposed to DetNet WG is
a variation of the well-known mechanism that has the same name.
Other well-known mechanisms that could provide bounded latency are
also covered, for example Deficit Round Robin (DRR) and Asynchronous
Traffic Shaping (ATS) [IEEE_802.1Qcr].
2. Terminology
2.1. Terms Used in This Document
2.2. Abbreviations
3. Conventions Used in This Document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
4. Taxonomy with Performance
Taxonomy based on the performance, such as E2E latency bounds and
jitter bounds, is helpful to understand the solutions. The
performance should be exhibited as a mathematical expression with the
network and traffic parameters.
4.1. Per Hop Dominant Factor for Latency Bound
One possible taxonomy would be based on the per hop dominant factor
for the latency bound. The dominant factor is defined as the largest
sum term in the expression, when the network and traffic conditions
are the worst. The worst condition typically means high network
utilization, large packet and burst sizes, and large number of hops.
Any existing solution can be put into one of three categories.
Category 1 (Max Packet Length/Service Rate): FQ and its variations
like C-SCORE fall into this category, where the latency bound is
primarily influenced by the ratio of a flow's maximum packet size to
its allocated service rate. This category emphasizes individual flow
isolation. The consequence is that the variation of E2E latency
bound for a flow is minimized with the other flows' join and leave.
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Therefore, this category performs well with dynamic flows. This
category also fits well to services with large bursts, since the
burst sizes of flows are not the dominant factor of the latency
bound.
Category 2 (Sum of Max Packet Lengths/Capacity): Solutions like DRR
belong here, where the dominant factor is the sum of maximum packet
lengths of all DetNet flows over the total allocated bandwidth. This
category typically has less implementation complexity than Category 1
but can impact individual flow isolation. The other flows' max
packet lengths affect the latency bound, which can be altered as
flows join and leave.
Category 3 (Sum of Max Burst Sizes/Capacity): CQF, TAS, their
variations (including CSQF, TCQF, ECQF, TQF), and EDF fall into this
category. The key influence on latency here is the total burst sizes
of all DetNet flows relative to the network capacity. This category
prioritizes bounded latency guarantees but may require tighter burst
control mechanisms. Once the burst is controlled, for example by an
extremely strict regulation, into a packet length level, then this
category may be indistinguishable with Category 2. This category
fits well to the services for static flows with small bursts.
As an example, assuming the capacities and maximum packet lengths are
identical in all the links along the path of a flow under
observation, the E2E latency bound of the flow by FQ is given as the
following [STILIADIS-LRS].
(B-L)/r + H(Lh/Rh + L/r), (1)
where B, L, and r are the maximum burst size of, the maximum packet
length of, and the allocated service rate to the flow, respectively;
H is the number of hops; Lh and Rh are the maximum packet length and
the capacity of all the links.
In this example, the term (Lh/Rh + L/r) can be seen as the per hop
latency, because the max burst size, B, appears only once. The
service rate of a flow, r, is likely to be much less than the link
capacity, Rh, while the maximum lengths L and Lh would not differ too
much. Therefore, the dominant factor here is L/r.
The dominant factor determines the level of flow isolation, as well
as the level of E2E latency bound value.
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5. Taxonomy with Functional Characteristics
Taxonomy based on the functional characteristics is the key to
understanding the solutions. The taxonomy listed in this section is
orthogonal to each other, if not stated explicitly.
5.1. Periodicity
If a solution transmits packets in a periodic pattern, in which a
packet is assigned to a time slot based on a predefined rule and a
set of consecutive time slots repeated periodically, then the
solution is periodic. Otherwise, the solution is non-periodic.
The set of consecutive time slots are called a period. Note that
here we use the term period to avoid confusion with the term cycle
used in CQF, which is equivalent to the time slot defined in this
draft.
According to the above definition, IEEE 802.1Qbv TAS is a periodic
solution. A finite Gate Control List (GCL) of TAS contains multiple
gate control entries. Each entry represents a time slot with an
assigned set of flows. A set of consecutive time slots forming a GCL
is repeated periodically. Time slots can be overlapped with each
other, as in ECQF.
TAS based solutions and CQF based solutions belong to periodic
solutions, for example CSQF, TCQF, ECQF, TQF and so on.
Periodic solutions may fit well to periodic services, and vice versa.
5.2. Network Synchronization
According to whether network synchronization is required, a solution
can be classified as either phase synchronous, frequency synchronous,
or asynchronous.
Phase synchronous solutions require network nodes to be both phase
and frequency synchronized. These solutions can be called strictly
synchronous. TAS and CQF are in this category.
Frequency synchronous solutions require network nodes to be only
frequency synchronized. Such nodes are often called syntonized. CQF
variations and TAS variations are in this category, for example CSQF,
TCQF, ECQF, TQF and so on.
Asynchronous solutions may also require loose phase and frequency
synchronizations, for example ATS and EDF.
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In non-synchronized networks, it has been shown that ignoring the
timing inaccuracies can lead to network instability due to unbounded
delay in per-flow or interleaved regulators [THOMAS-Sync]. However,
the level of synchronization required is not high. The problem can
be solved by adjusting the regulator parameters conservatively, even
when loosely synchronized clocks are used. Thus, the solutions that
require regulators such as ATS are categorized into asynchronous
solutions.
The criteria to distinguish between synchronous and asynchronous
solutions should be the level of required synchronization precision.
One indicator suitable to such criteria would be the allowable
Maximum Time Interval Error (MTIE). MTIE is usually calculated as
the difference between the largest and smallest time differences in
the ensemble of measurements. With this definition, a device that
has an arbitrarily large and constant time difference with the
standard reference has an MTIE value of 0, because MTIE is a measure
of the evolution of the time difference, not the magnitude of the
time difference itself. In this respect, the MTIE statistic is
really a measure of the frequency offset between the device under
test and the standard reference.
Therefore, the allowable MTIE value can be applied equivalently, for
the precision level evaluation, to both phase synchronous and
frequency synchronous solutions.
In a distributed system, typical MTIE can be managed within nano
second level. However, the exact value of the allowable MTIE as an
indicator for synchronous solutions is for further study. It is
expected to be within tens of nanoseconds.
Note that the taxonomy of network synchronization is closely related
to the taxonomy of periodicity. However, these two can be used
independently of each other.
5.3. Traffic Granularity
This draft categorizes data plane solutions based on the granularity
of their traffic control target, which refers to the size and
specificity of the traffic entity they handle. Three granularity
levels exist.
Flow level: Each packet is controlled based on its specific flow,
which can be identified usually by the 5-tuple. Examples include FQ
and its variations such as C-SCORE, which offer precise service
differentiation but require potentially complex implementation.
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Flow aggregate level: Flows are grouped by shared characteristics
like traffic specification, service requirement, or routing path.
This coarser level simplifies control but may offer less precise
differentiation. Examples include interleaved regulators in ATS.
Class level: Flows are further grouped by similar service
requirements, regardless of specific path or traffic details. This
coarsest level simplifies control and accommodates traffic
fluctuations but provides the least individual flow differentiation.
Typically, time or time based information could be used for
classification, such as in EDF, CQF and its variations.
For each level solution, packets within the same traffic entity
receive the same treatment. For example, if a solution is flow
aggregate level, then the packets within the same flow aggregate are
treated identically, regardless of the flows they belong to.
There are cases in which a single solution consists of multiple
functional entities that treat packets according to multiple traffic
entities of different granularities. In such cases, it is defined
that the functional entity with the coarsest granularity is dominant,
thus the whole solution belongs to the coarsest granularity category.
For example, ATS consists of interleaved regulators (IRs) and a
strict priority scheduler. An IR has a queue dedicated to a flow
aggregate having the same class and the same input port. The
regulation function itself is based on a flow. According to the
definition above, IR is a flow aggregate level solution. On the
other hand, the strict priority scheduler in ATS is class-based.
Therefore, ATS as a whole is class level.
A finer granularity level solution has a benefit of a more accurate
service differentiation among flows. Its limit is the larger
implementation complexity. It fits to services with flows having
various independent latency bound values.
Periodic solutions can further be categorized based on the traffic
granularity. A time slot can be assigned per flow, per flow
aggregate, or per class.
Note that TAS in 802.1Qbv is a scheduling mechanism defined in an
output port with eight queues. The queues are controlled by GCL and
its gate control entries. Each queue can serve a class. In an
entry, queues can be either open or closed. Thus, TAS can be seen as
a class level solution. However, in many cases TAS is understood as
a scheduling mechanism, where the number of queues are not limited to
8. There could be a natural extension, such as TQF, which enables
Qbv to allocate one queue to each flow or a flow aggregate.
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Finer granularity periodic solutions have more strengths in jitter
control. They also fit services with many periodic flows of
independent period values.
5.4. Work Conserving
A work conserving solution never idle when there is a packet to send
[Fedorova].
A non-work conserving solution can idle even if there is a packet to
send in the queue.
A solution can be a combination of multiple data plane functional
entities, and each functional entity has its own attribute of work
conserving or non-work conserving. A solution is non-work
conserving, as long as any of the functional entities included in the
solution has the non-work conserving attribute.
FIFO, round robin schedulers, FQ and its variations like C-SCORE are
examples of the work conserving solutions. TAS, CQF, ATS, and their
variations are non-work conserving solutions, for example CSQF, TCQF,
ECQF, TQF and so on. EDF can be operated either as work conserving
or non-work conserving.
Work conserving solutions have strengths in terms of average delay.
They usually show smaller observed maximum latencies than the
theoretical latency bound expressions suggest. They also benefit
from the statistical multiplexing gain without any wasted capacity,
thus more room for best effort traffic.
Non-work conserving solutions have strengths to avoid burst
accumulation and are also beneficial for jitter control. The burst
size of a flow can be kept similar or the same with the initial burst
size. Therefore, the buffer size necessary typically is less than
those in work conserving solutions. This further makes the latency
evaluation process simple.
5.5. Target Transmission Time
Data plane solutions can be categorized as "on-time" or "in-time"
based on how closely they adhere to predefined target transmission
times for packets.
On-time solutions strive to transmit packets as close as possible to
their target times without ever exceeding them. This ensures tight
control over both latency and jitter, but it can sometimes lead to
higher average latency.
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In-time solutions allow more flexibility, transmitting packets
without a specified target transmission time. FQ and its variations
are in-time solutions.
ATS, which includes the interleaved regulator, is an in-time
solution. A regulator determines an eligible time for a packet to be
transmitted. Packets are always transmitted at or later than their
eligible times. An eligible time is not a target transmission time.
Note that ATS is a non-work conserving but in-time solution.
TAS, CQF, and their variations are on-time solutions. A time slot of
TAS, within which a packet should be transmitted, can be seen as the
target interval. EDF can be operated either as in-time or on-time.
The on-time/in-time taxonomy here is about the scheduling decision,
which determines when a packet is transmitted. It is not about the
consequence of the scheduling, whether the jitter bound is also
guaranteed or not.
On-time solutions typically control the jitter as well as latency,
but suffer from larger average latency. In-time solutions have
limitations on controlling jitter. In-time solutions may have to
handle the jitter with additional mechanisms.
5.6. Service Order
Data plane solutions prioritize packets from different flows using
various decision rules, categorized as follows.
Rate-based: Packets are ordered based on the allocated service rate
of their flows or flow aggregates. Examples include FQ and its
variations like C-SCORE, and DRR.
Time-based: Packets are prioritized based on their allowed delay or
deadline. Examples are CQF, TAS, their variations, and EDF.
Arrival-based: Packets are served in the order they arrive. FIFO is
an example.
Priority-based: Packets are ordered based on assigned priorities.
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A solution can determine the service order of the packets from
different flows, based on a rule which considers the rate allocated
to a flow or a flow aggregate, the delay a packet is allowed, the
packet arrival time, or the packet priority. A rule may also be
constructed with a combination of these characteristics. Note that
the service order within a flow cannot be altered, thus is already
decided. We focus only on the service order among packets from
different flows.
According to its primary service order decision rule, a solution can
be categorized into either rate-based, time-based, arrival-based, or
priority-based. Any solution can also use the packet arrival time as
a secondary decision rule.
Strict priority scheduler uses primarily the priority of a packet.
It also uses the arrival times among packets of the same priority.
In this case it is categorized as priority-based.
ATS has IRs and a strict priority scheduler. The service order among
packets at an IR is arrival-based. The order among packets from
different input ports are decided at the strict priority scheduler.
Thus, ATS is priority-based.
Rate-based solutions have a simple admission condition check process
that is dependent only on the service rates of flows. They benefit
from the "pay burst only once" property, by which the maximum burst
size of a flow contributes to the E2E latency bound only once,
without being multiplied by the hop count. Rate-based solutions
typically fit well to services with large burst and large scale
services, without a need for overprovisioning, or additional burst
control mechanisms.
Time-based solutions have strengths in precise delay control for
packets or flows. The services with tight latency, small burst, and
small scale services may fit this category.
Priority-based and arrival-based solutions benefit from the
implementation simplicity. The latency and jitter differentiation
among flows can be coarse, however. The services with loose latency,
small burst, and non-periodic services may fit this category.
6. IANA Considerations
There might be matters that require IANA considerations associated
with metadata. If necessary, relevant text will be added in a later
version.
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7. Security Considerations
This section will be described later.
8. Acknowledgements
9. Contributor
10. References
10.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
10.2. Informative References
[Fedorova] Fedorova, A., Seltzer, M., and M.D. Smith, "A non-work-
conserving operating system scheduler for SMT processors",
In Proceedings of the Workshop on the Interaction between
Operating Systems and Computer Architecture, vol. 33, p.
10-17, June 2006.
[I-D.chen-detnet-sr-based-bounded-latency]
Chen, M., Geng, X., Li, Z., Joung, J., and J. Ryoo,
"Segment Routing (SR) Based Bounded Latency", Work in
Progress, Internet-Draft, draft-chen-detnet-sr-based-
bounded-latency-03, 7 July 2023,
<https://datatracker.ietf.org/doc/html/draft-chen-detnet-
sr-based-bounded-latency-03>.
[I-D.eckert-detnet-tcqf]
Eckert, T. T., Li, Y., Bryant, S., Malis, A. G., Ryoo, J.,
Liu, P., Li, G., Ren, S., and F. Yang, "Deterministic
Networking (DetNet) Data Plane - Tagged Cyclic Queuing and
Forwarding (TCQF) for bounded latency with low jitter in
large scale DetNets", Work in Progress, Internet-Draft,
draft-eckert-detnet-tcqf-05, 5 January 2024,
<https://datatracker.ietf.org/doc/html/draft-eckert-
detnet-tcqf-05>.
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[I-D.ietf-detnet-raw-industrial-req]
Sofia, R. C., Mendes, P., Bernardos, C. J., and E.
Schooler, "Requirements for Reliable Wireless Industrial
Services", Work in Progress, Internet-Draft, draft-ietf-
detnet-raw-industrial-req-00, 19 January 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-detnet-
raw-industrial-req-00>.
[I-D.ietf-detnet-scaling-requirements]
Liu, P., Li, Y., Eckert, T. T., Xiong, Q., Ryoo, J.,
zhushiyin, and X. Geng, "Requirements for Scaling
Deterministic Networks", Work in Progress, Internet-Draft,
draft-ietf-detnet-scaling-requirements-05, 20 November
2023, <https://datatracker.ietf.org/doc/html/draft-ietf-
detnet-scaling-requirements-05>.
[I-D.joung-detnet-stateless-fair-queuing]
Joung, J., Ryoo, J., Cheung, T., Li, Y., and P. Liu,
"Latency Guarantee with Stateless Fair Queuing", Work in
Progress, Internet-Draft, draft-joung-detnet-stateless-
fair-queuing-01, 19 October 2023,
<https://datatracker.ietf.org/doc/html/draft-joung-detnet-
stateless-fair-queuing-01>.
[I-D.peng-detnet-deadline-based-forwarding]
Peng, S., Du, Z., Basu, K., cheng, Yang, D., and C. Liu,
"Deadline Based Deterministic Forwarding", Work in
Progress, Internet-Draft, draft-peng-detnet-deadline-
based-forwarding-08, 14 December 2023,
<https://datatracker.ietf.org/doc/html/draft-peng-detnet-
deadline-based-forwarding-08>.
[I-D.peng-detnet-packet-timeslot-mechanism]
Peng, S., Liu, P., Basu, K., Liu, A., Yang, D., and G.
Peng, "Timeslot Queueing and Forwarding Mechanism", Work
in Progress, Internet-Draft, draft-peng-detnet-packet-
timeslot-mechanism-05, 14 December 2023,
<https://datatracker.ietf.org/doc/html/draft-peng-detnet-
packet-timeslot-mechanism-05>.
[IEEE_802.1Qcr]
IEEE, "IEEE Standard for Local and metropolitan area
networks - Bridges and Bridged Networks - Amendment 34:
Asynchronous Traffic Shaping", IEEE 802.1Qcr-2020,
DOI 10.1109/IEEESTD.2020.9253013, 6 November 2020,
<https://doi.org/10.1109/IEEESTD.2020.9253013>.
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[STILIADIS-LRS]
Stiliadis, D. and A. Anujan, "Latency-rate servers: A
general model for analysis of traffic scheduling
algorithms", IEEE/ACM Trans. Networking, vol. 6, no. 5,
pp. 611-624, 1998.
[THOMAS-Sync]
Thomas, L. and J-Y. Le Boudec, "On Time Synchronization
Issues in Time-Sensitive Networks with Regulators and
Nonideal Clocks", Proceedings of the ACM on Measurement
and Analysis of Computing Systems, vol. 4, no. 2, pp.
1-41, 2020.
Authors' Addresses
Jinoo Joung
Sangmyung University
Email: jjoung@smu.ac.kr
Xuesong Geng
Huawei
Email: gengxuesong@huawei.com
Shaofu Peng
ZTE Corporation
Email: peng.shaofu@zte.com.cn
Toerless Eckert
Futurewei Technologies
Email: tte@cs.fau.de
Joung, et al. Expires 28 August 2024 [Page 14]
