| RFC : | rfc9544 |
| Title: | DNS Security Extensions (DNSSEC) |
| Date: | March 2024 |
| Status: | INFORMATIONAL |
Internet Engineering Task Force (IETF) G. Mirsky
Request for Comments: 9544 J. Halpern
Category: Informational Ericsson
ISSN: 2070-1721 X. Min
ZTE Corp.
A. Clemm
J. Strassner
Futurewei
J. Francois
Inria and University of Luxembourg
March 2024
Precision Availability Metrics (PAMs) for Services Governed by Service
Level Objectives (SLOs)
Abstract
This document defines a set of metrics for networking services with
performance requirements expressed as Service Level Objectives
(SLOs). These metrics, referred to as "Precision Availability
Metrics (PAMs)", are useful for defining and monitoring SLOs. For
example, PAMs can be used by providers and/or customers of an RFC
9543 Network Slice Service to assess whether the service is provided
in compliance with its defined SLOs.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9544.
Copyright Notice
Copyright (c) 2024 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
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in the Revised BSD License.
Table of Contents
1. Introduction
2. Conventions
2.1. Terminology
2.2. Acronyms
3. Precision Availability Metrics
3.1. Introducing Violated Intervals
3.2. Derived Precision Availability Metrics
3.3. PAM Configuration Settings and Service Availability
4. Statistical SLO
5. Other Expected PAM Benefits
6. Extensions and Future Work
7. IANA Considerations
8. Security Considerations
9. Informative References
Acknowledgments
Contributors
Authors' Addresses
1. Introduction
Service providers and users often need to assess the quality with
which network services are being delivered. In particular, in cases
where service-level guarantees are documented (including their
companion metrology) as part of a contract established between the
customer and the service provider, and Service Level Objectives
(SLOs) are defined, it is essential to provide means to verify that
what has been delivered complies with what has been possibly
negotiated and (contractually) defined between the customer and the
service provider. Examples of SLOs would be target values for the
maximum packet delay (one-way and/or round-trip) or maximum packet
loss ratio that would be deemed acceptable.
More generally, SLOs can be used to characterize the ability of a
particular set of nodes to communicate according to certain
measurable expectations. Those expectations can include but are not
limited to aspects such as latency, delay variation, loss, capacity/
throughput, ordering, and fragmentation. Whatever SLO parameters are
chosen and whichever way service-level parameters are being measured,
Precision Availability Metrics indicate whether or not a given
service has been available according to expectations at all times.
Several metrics (often documented in the IANA "Performance Metrics"
registry [IANA-PM-Registry] according to [RFC8911] and [RFC8912]) can
be used to characterize the service quality, expressing the perceived
quality of delivered networking services versus their SLOs. Of
concern is not so much the absolute service level (for example,
actual latency experienced) but whether the service is provided in
compliance with the negotiated and eventually contracted service
levels. For instance, this may include whether the experienced
packet delay falls within an acceptable range that has been
contracted for the service. The specific quality of service depends
on the SLO or a set thereof for a given service that is in effect.
Non-compliance to an SLO might result in the degradation of the
quality of experience for gamers or even jeopardize the safety of a
large geographical area.
The same service level may be deemed acceptable for one application,
while unacceptable for another, depending on the needs of the
application. Hence, it is not sufficient to measure service levels
per se over time; the quality of the service being contextually
provided (e.g., with the applicable SLO in mind) must be also
assessed. However, at this point, there are no standard metrics that
can be used to account for the quality with which services are
delivered relative to their SLOs or to determine whether their SLOs
are being met at all times. Such metrics and the instrumentation to
support them are essential for various purposes, including monitoring
(to ensure that networking services are performing according to their
objectives) as well as accounting (to maintain a record of service
levels delivered, which is important for the monetization of such
services as well as for the triaging of problems).
The current state-of-the-art of metrics include, for example,
interface metrics that can be used to obtain statistical data on
traffic volume and behavior that can be observed at an interface
[RFC2863] [RFC8343]. However, they are agnostic of actual service
levels and not specific to distinct flows. Flow records [RFC7011]
[RFC7012] maintain statistics about flows, including flow volume and
flow duration, but again, they contain very little information about
service levels, let alone whether the service levels delivered meet
their respective targets, i.e., their associated SLOs.
This specification introduces a new set of metrics, Precision
Availability Metrics (PAMs), aimed at capturing service levels for a
flow, specifically the degree to which the flow complies with the
SLOs that are in effect. PAMs can be used to assess whether a
service is provided in compliance with its defined SLOs. This
information can be used in multiple ways, for example, to optimize
service delivery, take timely counteractions in the event of service
degradation, or account for the quality of services being delivered.
Availability is discussed in Section 3.4 of [RFC7297]. In this
document, the term "availability" reflects that a service that is
characterized by its SLOs is considered unavailable whenever those
SLOs are violated, even if basic connectivity is still working.
"Precision" refers to services whose service levels are governed by
SLOs and must be delivered precisely according to the associated
quality and performance requirements. It should be noted that
precision refers to what is being assessed, not the mechanism used to
measure it. In other words, it does not refer to the precision of
the mechanism with which actual service levels are measured.
Furthermore, the precision, with respect to the delivery of an SLO,
particularly applies when a metric value approaches the specified
threshold levels in the SLO.
The specification and implementation of methods that provide for
accurate measurements are separate topics independent of the
definition of the metrics in which the results of such measurements
would be expressed. Likewise, Service Level Expectations (SLEs), as
defined in Section 5.1 of [RFC9543], are outside the scope of this
document.
2. Conventions
2.1. Terminology
In this document, SLA and SLO are used as defined in [RFC3198]. The
reader may refer to Section 5.1 of [RFC9543] for an applicability
example of these concepts in the context of RFC 9543 Network Slice
Services.
2.2. Acronyms
IPFIX IP Flow Information Export
PAM Precision Availability Metric
SLA Service Level Agreement
SLE Service Level Expectation
SLO Service Level Objective
SVI Severely Violated Interval
SVIR Severely Violated Interval Ratio
SVPC Severely Violated Packets Count
VFI Violation-Free Interval
VI Violated Interval
VIR Violated Interval Ratio
VPC Violated Packets Count
3. Precision Availability Metrics
3.1. Introducing Violated Intervals
When analyzing the availability metrics of a service between two
measurement points, a time interval as the unit of PAMs needs to be
selected. In [ITU.G.826], a time interval of one second is used.
That is reasonable, but some services may require different
granularity (e.g., decamillisecond). For that reason, the time
interval in PAMs is viewed as a variable parameter, though constant
for a particular measurement session. Furthermore, for the purpose
of PAMs, each time interval is classified as either Violated Interval
(VI), Severely Violated Interval (SVI), or Violation-Free Interval
(VFI). These are defined as follows:
* VI is a time interval during which at least one of the performance
parameters degraded below its configurable optimal threshold.
* SVI is a time interval during which at least one of the
performance parameters degraded below its configurable critical
threshold.
* Consequently, VFI is a time interval during which all performance
parameters are at or better than their respective pre-defined
optimal levels.
The monitoring of performance parameters to determine the quality of
an interval is performed between the elements of the network that are
identified in the SLO corresponding to the performance parameter.
Mechanisms for setting levels of a threshold of an SLO are outside
the scope of this document.
From the definitions above, a set of basic metrics can be defined
that count the number of time intervals that fall into each category:
* VI count
* SVI count
* VFI count
These count metrics are essential in calculating respective ratios
(see Section 3.2) that can be used to assess the instability of a
service.
Beyond accounting for violated intervals, it is sometimes beneficial
to maintain counts of packets for which a performance threshold is
violated. For example, this allows for distinguishing between cases
in which violated intervals are caused by isolated violation
occurrences (such as a sporadic issue that may be caused by a
temporary spike in a queue depth along the packet's path) or by broad
violations across multiple packets (such as a problem with slow route
convergence across the network or more foundational issues such as
insufficient network resources). Maintaining such counts and
comparing them with the overall amount of traffic also facilitate
assessing compliance with statistical SLOs (see Section 4). For
these reasons, the following additional metrics are defined:
* VPC (Violated Packets Count)
* SVPC (Severely Violated Packets Count)
3.2. Derived Precision Availability Metrics
A set of metrics can be created based on PAMs as introduced in this
document. In this document, these metrics are referred to as
"derived PAMs". Some of these metrics are modeled after Mean Time
Between Failure (MTBF) metrics; a "failure" in this context refers to
a failure to deliver a service according to its SLO.
* Time since the last violated interval (e.g., since last violated
ms or since last violated second). This parameter is suitable for
monitoring the current compliance status of the service, e.g., for
trending analysis.
* Number of packets since the last violated packet. This parameter
is suitable for the monitoring of the current compliance status of
the service.
* Mean time between VIs (e.g., between violated milliseconds or
between violated seconds). This parameter is the arithmetic mean
of time between consecutive VIs.
* Mean packets between VIs. This parameter is the arithmetic mean
of the number of SLO-compliant packets between consecutive VIs.
It is another variation of MTBF in a service setting.
An analogous set of metrics can be produced for SVI:
* Time since the last SVI (e.g., since last violated ms or since
last violated second). This parameter is suitable for the
monitoring of the current compliance status of the service.
* Number of packets since the last severely violated packet. This
parameter is suitable for the monitoring of the current compliance
status of the service.
* Mean time between SVIs (e.g., between severely violated
milliseconds or between severely violated seconds). This
parameter is the arithmetic mean of time between consecutive SVIs.
* Mean packets between SVIs. This parameter is the arithmetic mean
of the number of SLO-compliant packets between consecutive SVIs.
It is another variation of "MTBF" in a service setting.
To indicate a historic degree of precision availability, additional
derived PAMs can be defined as follows:
* Violated Interval Ratio (VIR) is the ratio of the summed numbers
of VIs and SVIs to the total number of time unit intervals in a
time of the availability periods during a fixed measurement
session.
* Severely Violated Interval Ratio (SVIR) is the ratio of SVIs to
the total number of time unit intervals in a time of the
availability periods during a fixed measurement session.
3.3. PAM Configuration Settings and Service Availability
It might be useful for a service provider to determine the current
condition of the service for which PAMs are maintained. To
facilitate this, it is conceivable to complement PAMs with a state
model. Such a state model can be used to indicate whether a service
is currently considered as available or unavailable depending on the
network's recent ability to provide service without incurring
intervals during which violations occur. It is conceivable to define
such a state model in which transitions occur per some predefined PAM
settings.
While the definition of a service state model is outside the scope of
this document, this section provides some considerations for how such
a state model and accompanying configuration settings could be
defined.
For example, a state model could be defined by a Finite State Machine
featuring two states: "available" and "unavailable". The initial
state could be "available". A service could subsequently be deemed
as "unavailable" based on the number of successive interval
violations that have been experienced up to the particular
observation time moment. To return to a state of "available", a
number of intervals without violations would need to be observed.
The number of successive intervals with violations, as well as the
number of successive intervals that are free of violations, required
for a state to transition to another state is defined by a
configuration setting. Specifically, the following configuration
parameters are defined:
Unavailability threshold: The number of successive intervals during
which a violation occurs to transition to an unavailable state.
Availability threshold: The number of successive intervals during
which no violations must occur to allow transition to an available
state from a previously unavailable state.
Additional configuration parameters could be defined to account for
the severity of violations. Likewise, it is conceivable to define
configuration settings that also take VIR and SVIR into account.
4. Statistical SLO
It should be noted that certain SLAs may be statistical, requiring
the service levels of packets in a flow to adhere to specific
distributions. For example, an SLA might state that any given SLO
applies to at least a certain percentage of packets, allowing for a
certain level of, for example, packet loss and/or exceeding packet
delay threshold to take place. Each such event, in that case, does
not necessarily constitute an SLO violation. However, it is still
useful to maintain those statistics, as the number of out-of-SLO
packets still matters when looked at in proportion to the total
number of packets.
Along that vein, an SLA might establish a multi-tiered SLO of, say,
end-to-end latency (from the lowest to highest tier) as follows:
* not to exceed 30 ms for any packet;
* not to exceed 25 ms for 99.999% of packets; and
* not to exceed 20 ms for 99% of packets.
In that case, any individual packet with a latency greater than 20 ms
latency and lower than 30 ms cannot be considered an SLO violation in
itself, but compliance with the SLO may need to be assessed after the
fact.
To support statistical SLOs more directly requires additional
metrics, for example, metrics that represent histograms for service-
level parameters with buckets corresponding to individual SLOs.
Although the definition of histogram metrics is outside the scope of
this document and could be considered for future work (see
Section 6), for the example just given, a histogram for a particular
flow could be maintained with four buckets: one containing the count
of packets within 20 ms, a second with a count of packets between 20
and 25 ms (or simply all within 25 ms), a third with a count of
packets between 25 and 30 ms (or merely all packets within 30 ms),
and a fourth with a count of anything beyond (or simply a total
count). Of course, the number of buckets and the boundaries between
those buckets should correspond to the needs of the SLA associated
with the application, i.e., to the specific guarantees and SLOs that
were provided.
5. Other Expected PAM Benefits
PAMs provide several benefits with other, more conventional
performance metrics. Without PAMs, it would be possible to conduct
ongoing measurements of service levels, maintain a time series of
service-level records, and then assess compliance with specific SLOs
after the fact. However, doing so would require the collection of
vast amounts of data that would need to be generated, exported,
transmitted, collected, and stored. In addition, extensive post-
processing would be required to compare that data against SLOs and
analyze its compliance. Being able to perform these tasks at scale
and in real time would present significant additional challenges.
Adding PAMs allows for a more compact expression of service-level
compliance. In that sense, PAMs do not simply represent raw data but
expresses actionable information. In conjunction with proper
instrumentation, PAMs can thus help avoid expensive post-processing.
6. Extensions and Future Work
The following is a list of items that are outside the scope of this
specification but will be useful extensions and opportunities for
future work:
* A YANG data model will allow PAMs to be incorporated into
monitoring applications based on the YANG, NETCONF, and RESTCONF
frameworks. In addition, a YANG data model will enable the
configuration and retrieval of PAM-related settings.
* A set of IPFIX Information Elements will allow PAMs to be
associated with flow records and exported as part of flow data,
for example, for processing by accounting applications that assess
compliance of delivered services with quality guarantees.
* Additional second-order metrics, such as "longest disruption of
service time" (measuring consecutive time units with SVIs), can be
defined and would be deemed useful by some users. At the same
time, such metrics can be computed in a straightforward manner and
will be application specific in many cases. For this reason, such
metrics are omitted here in order to not overburden this
specification.
* Metrics can be defined to represent histograms for service-level
parameters with buckets corresponding to individual SLOs.
7. IANA Considerations
This document has no IANA actions.
8. Security Considerations
Instrumentation for metrics that are used to assess compliance with
SLOs constitutes an attractive target for an attacker. By
interfering with the maintenance of such metrics, services could be
falsely identified as complying (when they are not) or vice versa
(i.e., flagged as being non-compliant when indeed they are). While
this document does not specify how networks should be instrumented to
maintain the identified metrics, such instrumentation needs to be
adequately secured to ensure accurate measurements and prohibit
tampering with metrics being kept.
Where metrics are being defined relative to an SLO, the configuration
of those SLOs needs to be adequately secured. Likewise, where SLOs
can be adjusted, the correlation between any metric instance and a
particular SLO must be unambiguous. The same service levels that
constitute SLO violations for one flow and should be maintained as
part of the "violated time units" and related metrics may be
compliant for another flow. In cases when it is impossible to tie
together SLOs and PAMs, it is preferable to merely maintain
statistics about service levels delivered (for example, overall
histograms of end-to-end latency) without assessing which constitute
violations.
By the same token, the definition of what constitutes a "severe" or a
"significant" violation depends on configuration settings or context.
The configuration of such settings or context needs to be specially
secured. Also, the configuration must be bound to the metrics being
maintained. Thus, it will be clear which configuration setting was
in effect when those metrics were being assessed. An attacker that
can tamper with such configuration settings will render the
corresponding metrics useless (in the best case) or misleading (in
the worst case).
9. Informative References
[IANA-PM-Registry]
IANA, "Performance Metrics",
<https://www.iana.org/assignments/performance-metrics>.
[ITU.G.826]
ITU-T, "End-to-end error performance parameters and
objectives for international, constant bit-rate digital
paths and connections", ITU-T G.826, December 2002.
[RFC2863] McCloghrie, K. and F. Kastenholz, "The Interfaces Group
MIB", RFC 2863, DOI 10.17487/RFC2863, June 2000,
<https://www.rfc-editor.org/info/rfc2863>.
[RFC3198] Westerinen, A., Schnizlein, J., Strassner, J., Scherling,
M., Quinn, B., Herzog, S., Huynh, A., Carlson, M., Perry,
J., and S. Waldbusser, "Terminology for Policy-Based
Management", RFC 3198, DOI 10.17487/RFC3198, November
2001, <https://www.rfc-editor.org/info/rfc3198>.
[RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
"Specification of the IP Flow Information Export (IPFIX)
Protocol for the Exchange of Flow Information", STD 77,
RFC 7011, DOI 10.17487/RFC7011, September 2013,
<https://www.rfc-editor.org/info/rfc7011>.
[RFC7012] Claise, B., Ed. and B. Trammell, Ed., "Information Model
for IP Flow Information Export (IPFIX)", RFC 7012,
DOI 10.17487/RFC7012, September 2013,
<https://www.rfc-editor.org/info/rfc7012>.
[RFC7297] Boucadair, M., Jacquenet, C., and N. Wang, "IP
Connectivity Provisioning Profile (CPP)", RFC 7297,
DOI 10.17487/RFC7297, July 2014,
<https://www.rfc-editor.org/info/rfc7297>.
[RFC8343] Bjorklund, M., "A YANG Data Model for Interface
Management", RFC 8343, DOI 10.17487/RFC8343, March 2018,
<https://www.rfc-editor.org/info/rfc8343>.
[RFC8911] Bagnulo, M., Claise, B., Eardley, P., Morton, A., and A.
Akhter, "Registry for Performance Metrics", RFC 8911,
DOI 10.17487/RFC8911, November 2021,
<https://www.rfc-editor.org/info/rfc8911>.
[RFC8912] Morton, A., Bagnulo, M., Eardley, P., and K. D'Souza,
"Initial Performance Metrics Registry Entries", RFC 8912,
DOI 10.17487/RFC8912, November 2021,
<https://www.rfc-editor.org/info/rfc8912>.
[RFC9543] Farrel, A., Ed., Drake, J., Ed., Rokui, R., Homma, S.,
Makhijani, K., Contreras, L., and J. Tantsura, "A
Framework for Network Slices in Networks Built from IETF
Technologies", RFC 9543, DOI 10.17487/RFC9543, March 2024,
<https://www.rfc-editor.org/info/rfc9543>.
Acknowledgments
The authors greatly appreciate review and comments by Bjørn Ivar
Teigen and Christian Jacquenet.
Contributors
Liuyan Han
China Mobile
32 XuanWuMenXi Street
Beijing
100053
China
Email: hanliuyan@chinamobile.com
Mohamed Boucadair
Orange
35000 Rennes
France
Email: mohamed.boucadair@orange.com
Adrian Farrel
Old Dog Consulting
United Kingdom
Email: adrian@olddog.co.uk
Authors' Addresses
Greg Mirsky
Ericsson
Email: gregimirsky@gmail.com
Joel Halpern
Ericsson
Email: joel.halpern@ericsson.com
Xiao Min
ZTE Corp.
Email: xiao.min2@zte.com.cn
Alexander Clemm
Email: ludwig@clemm.org
John Strassner
Futurewei
2330 Central Expressway
Santa Clara, CA 95050
United States of America
Email: strazpdj@gmail.com
Jerome Francois
Inria and University of Luxembourg
615 Rue du Jardin Botanique
54600 Villers-les-Nancy
France
Email: jerome.francois@inria.fr
ERRATA