Telemetry Methodologies for Analog Measurement Instrumentation
draft-janzking-nmrg-telemetry-instrumentation-01

Internet Research Task Force                                     C. Janz
Internet-Draft                                             Huawei Canada
Intended status: Informational                                   D. King
Expires: 4 September 2024                           Lancaster University
                                                            3 March 2024

     Telemetry Methodologies for Analog Measurement Instrumentation
            draft-janzking-nmrg-telemetry-instrumentation-01

Abstract

   Evolution toward network operations automation requires systems
   encompassing software-based analytics and decision-making.  Network-
   based instrumentation provides crucial data for these components and
   processes.  However, the proliferation of such instrumentation and
   the need to migrate the data it generates from the physical network
   to "off-the-network" software, poses challenges.  In particular,
   analog measurement instrumentation, which generates time-continuous
   real number data, may generate significant data volumes.

   Methodologies for handling analog measurement instrumentation data
   will need to be identified and discussed, informed in part by
   consideration of requirements for the operation of network digital
   twins, which may be important software-realm consumers of such data.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Optical Network Measurement Instrumentation . . . . . . . . .   5
   5.  Telemetry Use Cases . . . . . . . . . . . . . . . . . . . . .   5
   6.  Analog Measurement Requirements . . . . . . . . . . . . . . .   6
     6.1.  Sampling  . . . . . . . . . . . . . . . . . . . . . . . .   6
     6.2.  Time Precision  . . . . . . . . . . . . . . . . . . . . .   7
     6.3.  Reduction and Other Pre-Processing  . . . . . . . . . . .   7
     6.4.  Compression . . . . . . . . . . . . . . . . . . . . . . .   7
     6.5.  Programmable Streaming  . . . . . . . . . . . . . . . . .   9
     6.6.  Streaming versus Polling  . . . . . . . . . . . . . . . .   9
     6.7.  Communication Protocols . . . . . . . . . . . . . . . . .  10
     6.8.  Data Models . . . . . . . . . . . . . . . . . . . . . . .  10
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  11
   8.  Operational Considerations  . . . . . . . . . . . . . . . . .  11
   9.  Security Considerations . . . . . . . . . . . . . . . . . . .  11
   10. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  12
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  12
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  12
     11.2.  Informative References . . . . . . . . . . . . . . . . .  12
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  13

1.  Introduction

   Existing studies for network telemetry typically deal with packet-
   oriented measurements for generating packet traffic, path, discard,
   latency and other data [RFC7799], [OPSAWG-IFIT-FRAMEWORK].  However,
   some networking equipment and network operations scenarios feature or
   use more physically-oriented measurement instrumentation that
   generates data of a different character.  Here, the particularities
   of data generated by such "analog" instrumentation are examined, and
   telemetry methodologies suitable for such data are considered.  This
   consideration is informed by the requirements of specific use cases,
   including network digital twins.

   Optical networks, which are increasingly rich in analog
   instrumentation, are used as a specific example here.  But the
   telemetry methodologies discussed may apply to instrumentation and
   telemetry intersecting a wide variety of networks and their related

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   operational software, for example, in support of digital twins that
   provide modeling of radio-based transmission, thermal characteristics
   or energy consumption.

   This document presents telemetry methodologies tailored for analog
   measurement instruments, aiming to enhance data accuracy,
   transmission efficiency, and real-time monitoring capabilities for
   network digital twins.  The findings underscore the potential of
   these methodologies to for best practice for telemetry digital twin
   networks that require analog measurement instruments.  It provides a
   state-of-the-art summary, including gaps and possible areas for
   further research

2.  Terminology

   Network Digital Twin:  A Network Digital Twin is a virtual replica of
      a physical network system that allows for the simulation,
      monitoring, and analysis of the network's behavior under various
      conditions without impacting the actual network.

   Network Measurement Instrumentation:  Network Measurement
      Instrumentation refers to the tools, techniques, and systems used
      to collect, monitor, and analyze data about the performance and
      behavior of a network.  This instrumentation is crucial for
      understanding how well the network is functioning, identifying
      problems, and making informed decisions to optimize network
      performance and reliability.

3.  Background

   Photonic networks, which transmit data through light signals via
   fiber optic cables, are fundamental to telecommunications, internet
   services, data center operations, and many other critical aspects of
   modern digital infrastructure.  A range of measurement instruments
   are routinely used in the deployment and maintenance of these
   networks.  Key examples include:

   The concept of network slicing is a key capability to serve a
   customer with a wide variety of different service needs expressed as
   SLOs/SLEs in terms of, e.g., latency, reliability, capacity, and
   service function-specific capabilities.

   This section outlines the key capabilities required to realize
   network slicing in a TE-enabled IETF technology network.

   *  Optical Time Domain Reflectometers (OTDRs): These devices are used
      to test the integrity of fiber optic cables by sending a series of
      light pulses into the fiber and measuring the light that is

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      scattered or reflected back.  OTDRs can detect and locate faults,
      splices, and bends in fiber optic cables, and are crucial for both
      installation and troubleshooting;

   *  Optical Spectrum Analyzers (OSAs): OSAs measure the power spectrum
      of optical devices to analyze the wavelength or frequency
      distribution of light.  They are vital for characterizing the
      performance of components like lasers and optical amplifiers
      within the network;

   *  Optical Power Meters and Light Sources: Used in tandem, these
      instruments measure the loss or attenuation in optical fibers and
      verify the power levels to ensure that signals are transmitted
      with sufficient strength without exceeding the damage threshold of
      the network components;

   *  Network Analyzers and Bit Error Rate Testers (BERTs): These tools
      assess the overall performance of the optical network by analyzing
      parameters such as signal integrity, bit error rates, and network
      latency.  They help in ensuring that the network can reliably
      handle the intended data loads;

   *  Wavelength Division Multiplexing (WDM) Analyzers: WDM technology
      combines multiple optical carrier signals on a single optical
      fiber by using different wavelengths.  WDM analyzers are
      specialized tools for testing and maintaining these systems,
      ensuring that each channel is transmitted efficiently without
      interference;

   *  Dispersion Analyzers: These are used to measure chromatic and
      polarization mode dispersion in fiber optic cables, which can
      affect the quality and speed of data transmission.  Managing
      dispersion is crucial for long-distance and high-data-rate optical
      communications.

   These instruments play a critical role in the characterization,
   deployment, optimization, and troubleshooting of optical networks.
   But their use tends to be restricted to specific operational phases,
   requires manual operation, and is generally not compatible with
   application to operating facilities.  The term instrumentation refers
   more properly to "embedded" capability that is both operable on
   active infrastructure and capable of continuous measurement
   operation.  Such instrumentation is a necessary foundation for
   telemetry

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4.  Optical Network Measurement Instrumentation

   Optical network instrumentation has typically focused on detecting
   transmission performance degradation, through measurement of error
   correction rates in FEC engines, counting of errored OTN frames, etc.
   Such measurements are typically executed on network elements through
   time-interval-based counting.  The resulting counts may be forwarded
   to or collected by software on a subscription or polling basis.  The
   data consists of series of integer numbers, or series of time stamp-
   integer number couplets.

   In recent years, however, the nature and scope of optical network
   instrumentation has broadened and deepened [JIANG].  The idea has
   been to instrument the optical network more richly to support more
   effective operations management, including using software-based
   analytics and modeling.  Implicated network operations include
   network and connection planning and configuration, network and
   connection fault management (fault and impairment detection,
   classification, localization, preemption, correction), and others.

   The optical network is a high-performance analog transmission
   network, so, unsurprisingly, much of this new instrumentation is
   analog; that is, it produces time-continuous real-number data or data
   sets.  Examples include optical loss, optical power (total, channel
   peak, etc.), optical spectra (narrow-band-filtered power measured at
   a series of center wavelengths), differential group delay (DGD),
   polarization mode dispersion (PMD), polarization dependent loss
   (PDL), Stokes vector components reflecting state of polarization
   (SOP), linear optical signal-to-noise ratio (OSNR) and generalized
   optical signal-to-noise ratio (GSNR).  Many of these measurements are
   synthesized by coherent receivers across the network, while some may
   be synthesized by in-span elements such as amplifiers and ROADMs.

5.  Telemetry Use Cases

   One application of this data in the software realm is with optical
   network digital twins (NDTs), used for transmission performance
   modeling [JANZ], [NMRG-PODTS].  Such NDTs constitute an important
   class of analytical engine supporting optical network and service
   planning and other operations, and they rely heavily on data from
   network instrumentation to enable accurate modeling of optical
   transmission performance on targeted variations of the actual network
   and service configuration, state and condition.  A default
   expectation would be that all instrumentation measurements are
   reflected continuously in the software realm for use by optical NDTs.
   However, at best only an approximation to this can be achieved (e.g.,
   only a series of sampled measurements may in fact be streamed from
   the network), so the imperative is to find efficient ways to support

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   sufficiently-accurate such approximations.  This imperative grows
   more compelling the greater the scale of the network and the greater
   the richness of embedded instrumentation.

   A second example application lies in the fault management domain,
   wherein analysis of rich data, concentrated around the time of a
   detected evolution in transmission conditions, may be used to
   classify and localize the origin of the observed evolution [HAHN].
   Transient evolutions of transmission performance are commonplace on
   optical networks and have myriad causes, including extrinsic causes
   such as lightning strikes, earthworks and construction, weather, road
   and rail traffic, fires, etc., as well as intrinsic causes including
   continuous or discrete deteriorations to equipment or fibre plant.
   Detection, classification, and localization of transmission
   performance evolutions permit assessment of the likelihood, expected
   severity, and rate of further deterioration, and planning of timely
   and cost-effective corrective interventions where indicated.
   However, successful analysis may depend on the availability of richer
   data sets in software that may be supported by continuous streaming
   or required by other applications.

6.  Analog Measurement Requirements

   [RFC9232]provides a framework for considering concepts, constructs
   and developments in network telemetry.  Many of the methods and
   mechanisms it discusses or suggests are invoked here.

6.1.  Sampling

   An analog-to-digital conversion process typically converts analog
   signals into digital data that can be transmitted, stored, and
   processed more efficiently.  This often involves sampling the signal
   at a certain rate and quantizing the amplitude into digital values.
   The "mirroring" (transmission for replication at a different place)
   of continuous-time real number data, generated by in-network
   instrumentation, begins with sampling and representing measured
   values by a scalar or vector of finite-decimal-place numbers.  As
   neither sampling at fixed intervals, nor fixed time alignment or
   offset among measurement points in the network or between such points
   and the off-network software realm, can generally be assumed; it is
   useful that instrumentation should generate, as primary data, a
   series of couplets or vectors consisting of sample time stamps and
   corresponding measured data values.

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6.2.  Time Precision

   Inadequate sampling frequency and quantization error are both
   potential sources of error, in the - literal or effective -
   "reconstruction"" of the original time-continuous measurement in the
   software realm.  It is possible that sampling frequencies might be
   varied in response to evolving temporal characteristics of measured
   parameters; this is one strategy for data reduction (and one reason
   why sampling may not occur at fixed-period intervals).

   Requirements on the precision of reconstructed data, its time basis,
   and the alignment in time of different reconstructed measurements;
   are determined by the operational role played by the analytical
   functions that consume the data.  Some operations of interest, such
   as network and service planning or fault and impairment management,
   may impose only relatively relaxed requirements on time
   synchronization among measurement instruments, and between those
   instruments and the software realm.  Other applications, e.g., those
   concerning operations tending toward closed loop control, may require
   tighter temporal data alignment among different measurement sources.
   These considerations have implications in terms of source and
   synchronization of clocks producing time stamps; but in general,
   requirements on clock synchronization and precision are far from
   those required for bit-level operations: i.e. they are generally more
   like "network time" than "digital time".

   Similarly, requirements on the absolute or relative (i.e. among
   different measurement instruments) precision of reconstructed
   measured data values may be application-dependent.  In many cases,
   relative precision, or precision consistency, may be more important
   than absolute precision.

6.3.  Reduction and Other Pre-Processing

   With telemetric data volume a primary potential challenge, methods
   for reducing data volume associated with analog measurement
   instrumentation are of evident interest.  Signals may also be
   filtered to remove noise and unwanted frequencies to improve the data
   quality.

6.4.  Compression

   Data compression is an obvious candidate methodology for bandwidth
   reduction.  Methods for lossless compression of series of numerical
   data have been widely studied, e.g.  [RATANAWORABHAN].

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   Obviously, such compression must be implemented as a "pre-processing"
   function executed by the telemetric instrumentation itself, or some
   proxy to it.  Similarly, decompression must be implemented as a
   "post-processing" function within the software realm.  Where time
   stamps are uncompressed, depending on the compression methodology
   employed, it may be possible to support selective decompression of
   data, e.g., only on selected time intervals.  This might allow for
   application-driven "as-required" post-processing (decompression) of
   more limited volumes of telemetric data.

   The compressibility of time-based data depends on its evolution in
   data-entropic terms, resulting in streamed data flows of varying
   volume or rate.  The effective transmission and reception rates of
   data samples thus may vary and differ at any point from the rate of
   data generation.  This is another reason why data samples may require
   time stamps.

   Other forms of effective data reduction through pre-processing may
   also be useful, or preferred:

   *  Thresholding: Data samples are transmitted only if and when a
      measured value, or a derivative of the measured value, crosses a
      threshold.  Possible examples include: a) exceeding some absolute
      or proportional variation from the last transmitted sample value;
      b) exceeding a previously observed and transmitted maximum or
      minimum value; or, c) exceeding some time rate-of-change of the
      measured value.

   Post-processing of threshold-driven data may or may not be required
   by applications.  For example, an application may generate a scenario
   for behavioral analysis by an NDT that requires the "current" data
   from network instrumentation.  To whatever precision is effectively
   reflected in the details of the operating thresholding mechanisms,
   that data is simply the most recently transmitted sample from network
   measurement instruments.  Another application, however, perhaps one
   dealing with fault or impairment management, might require a regular
   and continuous time series presentation of measured data.  In that
   case, e.g. interpolation or other post-processing of received data
   samples might be needed.

   Other kinds of pre-processing may also be interest, including
   normalization of data, frequency domain conversion, and computation
   of statistics.

   *  Triggering: An extension or variation of thresholding, triggering
      may refer to, e.g. the transmission of a series of samples - from
      a defined set of measurement instruments, over a defined period of
      time and at defined time intervals - on crossing of a particular

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      threshold (i.e., that threshold crossing "triggers" the
      transmission of the defined data series).  Triggering of this kind
      may be useful in e.g. fault and impairment management.  The
      detection by instrumentation of some pre-defined circumstance or
      occurrence - e.g. observation of an unusually large or rapid
      change in an optical power level or channel SOP - would trigger
      the transmission of a pre-defined, "rich" set of data covering a
      time interval around the triggering observation.  That data could
      then be subjected to various forms of "forensic" analysis in
      software to support detection, classification or localization of
      transmission performance-impacting events.  Required pre-
      processing includes processing of triggers, and the sliding
      storage of instrumentation data sample values sufficient to cover
      the targeted data capture time "window" as well as trigger
      processing and transmission intervals.

6.5.  Programmable Streaming

   As discussed in [RFC9232], in-network pre-processing of telemetry
   data may usefully be "programmed" by telemetry clients (i.e.,
   software applications that are consumers of instrumentation data),
   including dynamically or variably.  The range and nature of software
   applications and their data requirements may vary among systems, may
   evolve with time within any given system - based on experience and
   learning (automated or not) or with the deployment of new
   capabilities - and may also vary as a function of available
   instrumentation capabilities on a given network, which themselves may
   evolve.

6.6.  Streaming versus Polling

   Streaming - i.e., subscription-based push - is, as identified in
   [RFC9232] and other works, and as suggested by the discussion above,
   expected to be the principal, if not exclusive, operational modality
   for telemetry, including analog instrumentation telemetry.  Software
   clients consume data generated by the network, and having identified
   which data they require and from where within the network, use
   subscriptions to place themselves in a position to receive it, on an
   ongoing basis, without continuing operational steps.

   Triggered transmission of "batched" data is aligned with a streaming
   paradigm, as the telemetry server (i.e., instrumentation) must detect
   the trigger conditions and react by capturing and transmitting data
   to subscribing clients.

   It is worth considering, however, whether polling can or should be
   completely dispensed with, or whether it might retain some utility in
   some cases or circumstances.

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   The discussion so far supports a view that the data needs of NDTs can
   be satisfied, and in fact probably are best served by, streaming.
   However, polling could be used if NDT-based analyses are required
   relatively infrequently, do not require very rapid execution, and do
   not draw arbitrarily on historical data.  Polling might also be
   useful as a complementary mechanism to streaming.  For example, to
   reduce data transmission and handling volumes, an NDT might choose to
   unsubscribe from telemetry it has observed changes little with time.
   However, for particularly critical analyses, the NDT might want to
   ensure that all available telemetry data is up-to-date, by polling
   the unsubscribed instrumentation.  Further, if certain kinds of data
   compression are used, decompression processes can enter into errored
   regimes e.g. through transmission loss of telemetry data.  Periodic
   polling may be useful to "re-set" absolute data values in such cases.
   In fact, as suggested in [RFC7799], the possibility of transmission
   loss of streamed telemetry packets, a concern particularly if
   unreliable transport paradigms such as UDP are used, may provide a
   general reason to enable polling as a "failsafe" mechanism.

6.7.  Communication Protocols

   Communication protocols facilitate the reliable data exchange between
   telemetry devices and control systems.  Depending on the method,
   streaming and/or polling, various messaging protocols exist to
   provide efficient delivery of instrumentation data.

6.8.  Data Models

   A complete framework for analog instrumentation telemetry might
   require data models supporting:

   *  Identification of instrumentation-equipped and telemetry-capable
      network equipment, the latter's available instrumentation, its
      available pre-processing, and what aspects of available pre-
      processing are programmable;

   *  Subscription to streaming from specific instrumentation;

   *  Programming (or re-programming) of pre-processing on specific
      subscriptions and instrumentation, including type of pre-
      processing, applicable thresholds or triggers, and definition of
      trigger-associated data sets (included data and start/stop
      interval limits vs. triggering events);

   *  Transmission of applicable time stamp-data value couplets, vectors
      or batches.

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7.  IANA Considerations

   This document makes no requests for action by IANA.

8.  Operational Considerations

   Operational considerations for Optical Network Measurement
   Instrumentation involve a range of factors to ensure accurate,
   reliable, and efficient performance of the optical networks.  These
   considerations are critical for deploying, maintaining, and
   troubleshooting fiber optic systems.  Key operational considerations
   include:

   *  Calibration and Signal Integrity

   *  Dynamic Range and Sensitivity

   *  Resolution and Accuracy

   *  Scalability

   *  Bandwidth and storage of instrumentation data

   Future version of this document will expand on the topics above and
   increase the scope of operational considerations.

9.  Security Considerations

   The security implications of optical network telemetry are critical,
   given the increasing reliance on optical networks for data
   transmission in various sectors.  Ensuring the security and integrity
   of these networks and thetelemetry instrumentation used to measure
   and maintain them is paramount to prevent unauthorized access, data
   breaches, potential service disruptions, and use as possible threat
   vectors and attack surfaces.

   Key security considerations include:

   *  Encryption of sensitive telemetry data

   *  Secure configuration and management of telemetry functions

   *  Network monitoring and anomaly detection

   *  Secure data handling and storage

   Future version of this document will expand on the topics above and
   increase the scope of security considerations.

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10.  Acknowledgements

   Thanks to discussions in the Network Digital Twin discussions Network
   Management Research Group that provided further input into this work.

   This work is supported by the UK Department for Science, Innovation
   and Technology under the Future Open Networks Research Challenge
   project TUDOR (Towards Ubiquitous 3D Open Resilient Network).  The
   views expressed are those of the authors and do not necessarily
   represent the project

11.  References

11.1.  Normative References

11.2.  Informative References

   [HAHN]     Optical Fiber Communications, "On the Spatial Resolution
              of Location-Resolved Performance Monitoring by Correlation
              Method", 1 March 2023.

   [JANZ]     IEEE/IFP Network Operations and Management Symposium,
              Workshop of Technologies for Network Twins, "Digital Twin
              for the Optical Network: Key Technologies and Enabled
              Automation Applications", 1 April 2022,
              <https://ieeexplore.ieee.org/document/9789844>.

   [JIANG]    Journal of Lightwave Technology, vol. 40, No. 10, pp.
              3128-3136, "Progresses of Pilot Tone Based Optical
              Performance Monitoring in Coherent Systems", 1 October
              2023, <https://opg.optica.org/jlt/abstract.cfm?uri=jlt-
              40-10-3128>.

   [NMRG-PODTS]
              IETF, "Performance-Oriented Digital Twins for Packet and
              Optical Networks", 1 October 2023,
              <https://datatracker.ietf.org/doc/draft-paillisse-nmrg-
              performance-digital-twin/02>.

   [OPSAWG-IFIT-FRAMEWORK]
              IETF, "Framework for In-Situ Flow Information Telemetry",
              1 October 2023, <https://datatracker.ietf.org/doc/html/
              draft-song-opsawg-ifit-framework-21>.

   [RATANAWORABHAN]
              Data Compression Conference, "Fast Lossless Compression of
              Scientific Floating-Point Data", 1 May 2006.

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   [RFC7799]  Morton, A., "Active and Passive Metrics and Methods (with
              Hybrid Types In-Between)", RFC 7799, DOI 10.17487/RFC7799,
              May 2016, <https://www.rfc-editor.org/info/rfc7799>.

   [RFC9232]  Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
              A. Wang, "Network Telemetry Framework", RFC 9232,
              DOI 10.17487/RFC9232, May 2022,
              <https://www.rfc-editor.org/info/rfc9232>.

Authors' Addresses

   Chris Janz
   Huawei Canada
   Email: christopher.janz@huawei.com

   Daniel King
   Lancaster University
   Email: d.king@lancaster.ac.uk

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