Internet-Draft PAM for Multi-SLO November 2022
Mirsky, et al. Expires 11 May 2023 [Page]
Network Working Group
Intended Status:
Standards Track
G. Mirsky
J. Halpern
X. Min
ZTE Corp.
A. Clemm
J. Strassner
J. Francois

Precision Availability Metrics for SLO-Governed End-to-End Services


This document defines a set of metrics for networking services with performance requirements expressed as Service Level Objectives (SLO). These metrics, referred to as Precision Availability Metrics (PAM), are useful for defining and monitoring of SLOs. Specifically, PAM can be used by providers and/or users of the Network Slice service to assess whether the service is provided in compliance with its specified quality, i.e., in accordance with its defined SLOs.

Status of This Memo

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This Internet-Draft will expire on 11 May 2023.

1. Introduction

Network operators and network users often need to assess the quality with which network services are being provided and delivered. In particular in cases where service level guarantees are given and service level objectives (SLOs) are defined, it is essential to provide a measure of the degree with which actual service levels that are delivered comply with SLOs that were agreed, typically in a contract or agreement. Examples of service levels include service latency and packet loss. Simple examples of SLOs associated with such service levels 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.

An example of an SLO is one that characterizes the continued ability of a particular set of nodes to communicate. Essentially, the absence of what is, in other contexts, is called a defect. The SLO would include the various time and measurement aspects that would be interpreted as a defect or failure to communicate. It is important to note that it is being defined as a state, and thus, it has conditions that define entry into it and exit out of it. It is expected that a Service Level Agreement (SLA) includes a defect-related SLO, possibly in addition to other SLOs.

To express the perceived quality of delivered networking services versus their SLOs, a set of metrics are needed to characterize the quality of the service being provided. Of concern is not so much the absolute service level (for example, actual latency experienced), but whether the service is provided in accordance with the negotiated, and eventually contracted, service levels. For instance, this may include whether the packet delay that is experienced falls within an acceptable range that has been contracted for the service. The specific quality of service depends on the SLO that is in effect. A non-conformance to an SLO might result in degradation of the quality of experience for gamers or even jeopardize the safety of a large geographical area. However, as those applications represent clear business opportunities, they demand dependable technical solutions.

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 simply measure service levels per se over time, but to assess the quality of the service being provided with the applicable SLO in mind. However, at this point, there are no standard metrics in place that can be used to account for the quality with which services are delivered relative to their SLOs, and whether their SLOs are being met at all times. Such metrics and the instrumentation to support them are essential for a number of 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, important for monetization of such services as well as for triaging of problems).

The current state-of-the-art of metrics available today includes, for example, interface metrics, useful to obtain data on traffic volume and behavior that can be observed at an interface [RFC2863] and [RFC8343], but agnostic of actual service levels and not specific to distinct flows. Flow records [RFC7011] and [RFC7012] maintain statistics about flows, including flow volume and flow duration, but again, contain very little information about end-to-end service levels, let alone whether the service levels delivered to meet their targets, i.e., their associated SLOs.

This specification introduces a new set of metrics, Precision Availability Metrics (PAM), aimed at capturing end-to-end service levels for a flow, specifically the degree to which flows comply with the SLOs that are in effect. PAM can be used to assess whether a service is provided in compliance with its specified quality, i.e., in accordance 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 the fact that services whose end-to-end service levels are governed by SLOs, and which must therefore be precisely delivered 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, only applies when the metric value approaches the specified threshold levels in the SLO. The specification and implementation of methods that provide for accurate measurements is a separate topic independent of the definition of the metrics in which the results of such measurements would be expressed.

Service Level Expectations (SLEs), as defined in Section 4.1 of [I-D.ietf-teas-ietf-network-slices], are outside the scope of this document, because it is in the nature of SLEs that they define parts of the SLA that are not easily measured.

[Ed.note: It should be noted that at this point, the set of metrics proposed here is intended as a "starter set" that is intended to spark further discussion. Other metrics are certainly conceivable; we expect that the list of metrics will evolve as part of the Working Group discussions.]

2. Conventions and Terminology

2.1. Terminology

In this document, SLA and SLO are used as defined in Section 4.1 [I-D.ietf-teas-ietf-network-slices].

2.2. Acronyms

PAM Precision Availability Metric

OAM Operations, Administration, and Maintenance

SLA Service Level Agreement

SLE Service Level Expectations

SLO Service Level Objective

VI Violated Interval

VIR Violated Interval Ratio

SVI Severely Violated Interval

SVIR Severely Violated Interval Ratio

VFI Violation-Free Interval

3. Precision Availability Metrics

3.1. Introducing Violated Intervals

When analyzing the availability metrics of a service flow between two nodes, we need to select a time interval as the unit of PAM. In [ITU.G.826], a time interval of one second is used. That is reasonable, but some services may require different granularity. For that reason, the time interval in PAM is viewed as a variable parameter though constant for a particular measurement session. Further, for the purpose of PAM, each time interval, e.g., second or decamillisecond, is classified either as 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 pre-defined optimal level threshold.
  • SVI is a time interval during which at least one the performance parameters degraded below its pre-defined critical threshold.
  • Consequently, VFI is a time interval during which all performance objectives are at or better than their respective pre-defined optimal levels.

Mechanisms of setting levels of threshold of an SLO are outside the scope for this document.

From these defitions, a set of basic metrics can be defined that count the numbers 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 the service.

3.2. Derived Precision Availability Metrics

A set of metrics can be created based on PAM introduced in Section 3. In this document, these metrics are referred to as derived PAM. Some of these metrics are modeled after Mean Time Between Failure (MTBF) metrics - a "failure" in this context referring to a failure to deliver a packet according to its SLO.

  • Time since the last violated interval (e.g., since last violated ms, since last violated second). (This parameter is suitable for monitoring the current compliance status of the service, e.g., for trending analysis.)
  • 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, violated seconds) is the arithmetic mean of time between consecutive VIs.
  • Mean packets between VIs is the arithmetic mean of the number of SLO-compliant packets between consecutive VIs. (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, since last violated second). (This parameter is suitable for the monitoring of the current compliance status of the service.)
  • 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, severely violated seconds) is the arithmetic mean of time between consecutive SVIs.
  • Mean packets between SVIs is the arithmetic mean of the number of SLO-compliant packets between consecutive SVIs. (Another variation of "MTBF" in a service setting.)

Determining the current condition of the monitored service with respect to availability/unavailability is helpful. But because the transition between service availability/unavailability periods is based on a pre-defined number of consecutive intervals, e.g., ten, shorter conditions may not be adequately reflected. Two additional PAMs can be used, and they are defined as follows:

  • violated interval ratio (VIR) is the ratio of the combined number of VIs and SVIs to the total number of time unit intervals in a time of the availability periods during a fixed measurement interval.
  • 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 interval.

3.3. Service Availability in PAMs

VI, SVI, and VFI characterize the communication between two nodes relative to the level of required and acceptable performance and when the performance level degrades below an acceptable level. The former condition in this document defined to as service availability. The latter is defined as service unavailability. Based on the definitions in Section 3.1, SVI is the one time interval of service unavailability while VI and VFI present an interval of service availability. Since the conditions of the service are are continually changing, periods of availability and unavailability need to be defined with duration larger than one time interval to reduce the number of state changes while correctly reflecting the service condition.

It is worth noting that a composite service might include a set of connectivity constructs. An SLO might apply to all the constructs, or some constructs are assigned different sets of SLOs. For the purpose of PAM, each connectivity construct that composes the service can be monitored for its own SLO conformance as a sub-service. The composition of PAMs of these sub-services can be viewed as the PAM of the composite service.

The method to determine the state of the service in terms of PAM is described below:

  • If ten consecutive SVIs been detected, then the PAM state of the service is defined as unavailability, and the beginning of that period of unavailability state is at the start of the first SVI in the sequence of the consecutive SVIs.
  • Similarly, for ten consecutive non-SVIs (i.e., either VIs or VFIs), the service is defined to be available. The start of that period is at the beginning of the first non-SVI.
  • Resulting from these two definitions, a sequence of less than ten consecutive SVIs or non-SVIs does not change the PAM state of the service. For example, if the PAM state is determined as unavailable, a sequence of seven VFI s is not viewed as an availability period.

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 an SLO of, say, end-to-end latency to not exceed 20 ms for 99% of packets, to not exceed 25ms for 99.999% of packets, and to never exceed 30ms for any packet. In that case, any individual packet with latency larger 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, such as metrics that represent histograms for service level parameters with buckets corresponding to individual service level objectives. For the example just given, a histogram for a given flow could be maintained with three buckets: one containing the count of packets within 20ms, a second with a count of packets between 20 and 25ms (or simply all within 25ms), a third with a count of packets between 25 and 30ms (or merely all packets within 30ms, 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. The definition of histogram metrics is for further study (see Section 6).

5. Other PAM Benefits

PAM provides a number of benefits with other, more conventional performance metrics. Without PAM, it would be possible to conduct ongoing measurements of service levels and maintain a time-series of service level records, 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 postprocessing 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 PAM allows for a more compact expression of service level compliance. In that sense, PAM does not simply represent raw data but expresses actionable information. In conjunction with proper instrumentation, PAM can thus help avoid expensive postprocessing.

6. Discussion Items

The following items require further discussion:

  • Metrics. The foundational metrics defined in this draft refer to violated intervals. In addition, counts of violations related to individual packets may also need to be maintained. Metrics referring to violated packets (i.e., packets that on an individual basis miss a performance objective) may be added in a later revision of this document.

The following is a list of items for which further discussion is needed as to whether they should be included in the scope of this specification:

  • A YANG data model.
  • A set of IPFIX Information Elements.
  • Statistical metrics: e.g., histograms/buckets.
  • Policies regarding the definition of "violated" and "severely violated" time interval.
  • Additional second-order metrics, such as "longest disruption of service time" (measuring consecutive time units with SVIs).

7. IANA Considerations

This document has no IANA actions.

8. Security Considerations

Instrumentation for metrics that are used to assess compliance with SLOs constitute an attractive target for an attacker. By interfering with the maintaining 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 metrics instance and a particular SLO must be clear. The same service levels that constitute SLO violations for one flow that should be maintained as part of the "violated time units" and related metrics, may be perfectly compliant for another flow. In cases when it is impossible to tie together SLOs and PAM properly, it will be preferable to merely maintain statistics about service levels delivered (for example, overall histograms of end-to-end latency) without assessing which constitutes violations.

By the same token, where the definition of what constitutes a "severe" or a "significant" violation depends on policy or context. The configuration of such policy or context needs to be specially secured. Also, the configuration of this policy must be bound to the metrics being maintained. This way, it will be clear which policy was in effect when those metrics were being assessed. An attacker that can tamper with such policies will render the corresponding metrics useless (in the best case) or misleading (in the worst case).

9. Acknowledgments


10. References

10.1. Informative References

Farrel, A., Drake, J., Rokui, R., Homma, S., Makhijani, K., Contreras, L. M., and J. Tantsura, "Framework for IETF Network Slices", Work in Progress, Internet-Draft, draft-ietf-teas-ietf-network-slices-16, , <>.
ITU-T, "End-to-end error performance parameters and objectives for international, constant bit-rate digital paths and connections", ITU-T G.826, .
McCloghrie, K. and F. Kastenholz, "The Interfaces Group MIB", RFC 2863, DOI 10.17487/RFC2863, , <>.
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, , <>.
Claise, B., Ed. and B. Trammell, Ed., "Information Model for IP Flow Information Export (IPFIX)", RFC 7012, DOI 10.17487/RFC7012, , <>.
Boucadair, M., Jacquenet, C., and N. Wang, "IP Connectivity Provisioning Profile (CPP)", RFC 7297, DOI 10.17487/RFC7297, , <>.
Bjorklund, M., "A YANG Data Model for Interface Management", RFC 8343, DOI 10.17487/RFC8343, , <>.

Contributors' Addresses

Liuyan Han
China Mobile
32 XuanWuMenXi Street
Mohamed Boucadair
35000 Rennes
Adrian Farrel
Old Dog Consulting
United Kingdom

Authors' Addresses

Greg Mirsky
Joel Halpern
Xiao Min
ZTE Corp.
Alexander Clemm
2330 Central Expressway
Santa Clara, CA 95050
United States of America
John Strassner
2330 Central Expressway
Santa Clara, CA 95050
United States of America
Jerome Francois
615 Rue du Jardin Botanique
54600 Villers-les-Nancy