Internet-Draft Sustainability Insights May 2024
Andersson, et al. Expires 8 November 2024 [Page]
Network Working Group
Intended Status:
P. Andersson
Cisco Systems
J. Lindblad
Cisco Systems
S. Mitrovic
Cisco Systems
M. Palmero
Cisco Systems
E. Roure
Cisco Systems
G. Salgueiro
Cisco Systems
E. Stephan

Sustainability Insights


Internet and the ICT industry is consuming a sizable portion of the electricity available in the world today, and the fraction has been growing significantly over time. Data shows that the power draw of internet is relatively constant over the day and week, even though the “load” and delivered services vary greatly over this time span. This seems to suggest that there is room for optimizations.

This document provides some definitions, some proposed principles for a solution, and then paints a picture of what a solution based on existing standards and some current internet drafts might look like.

The first step of an optimization loop is to measure. This document proposes a mechanism to collect energy related telemetry data from the extremely diverse set of devices found in networks today, without necessarily updating the network elements. Once the collection is done, it’s also very relevant to be able to control the devices, and turn them into low power modes when the service demand is lower.

Status of This Memo

This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79.

Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at

Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress."

This Internet-Draft will expire on 8 November 2024.

Table of Contents

1. Introduction

To answer questions about how sustainable equipment and operational practices are, various key performance indicators (KPIs) produced by network devices, management systems, and networking solutions are necessary. While such KPIs are abundantly produced and collected today there are quite a few issues with their usability and commonality. Without a common definition of metrics across the industry and widespread adoption, we will be left with ill-defined, potentially redundant, and proprietary metrics.

An aspect lacking today is the precise definitions of the collected metrics. This leads to KPIs that are not comparable to each other, as it is unknown what is included in the outcomes and what is not. It makes it challenging to sum or compare numbers from different manufacturers and organizations without investing in data normalization and a high number of assumptions.

To produce aggregate data, it is also important to consider how the component inputs are combined. Different vendors and operators might do this aggregation differently, yet again producing values that are hard to combine or compare when also using different units of measurement. In many cases, one might suspect the actual numbers are underestimated, since there is competitive pressure to produce small numbers to report on the environmental impact of Internet communications and applications in contrast with the benefit of using it. The aim shall not be to “produce the numbers” but to find quantitative measures, when possible, that give a fair assessment of Sustainability related metrics vs. useful work.

It may be tempting to define the useful work in networking equipment as simply as the number of bits that are passing through the device. For some types of equipment, that might be appropriate, but clearly a video system that is sending a video stream with better video compression is not necessarily less sustainable just because it sends fewer bits per Joule. There are also many kinds of networking equipment where measuring the end user value in number of passed bits is obviously ridiculous, and other metrics have to be defined. Monitoring or management systems are examples of this.

Another important and key aspect, when referring to environmental impact metrics is what needs to be considered as part of the lifecycle. Life cycle assessment, also known as LCA, of networks and services, is defined by ISO 14040 as the compilation and evaluation of the inputs, outputs, and potential environmental impacts throughout its lifecycle.

LCA is based on four main phases:

This document is setting up the stage to identify data quality requirements, under the information and communications technology (ICT) category. Following product Lifecycle Accounting (LCA), this document focuses on using the five product lifecycle stages defined by the GHG Protocol Accounting and Reporting Standard, which is in accordance with the ISO 14040:44 standards:

  1. Use

  2. Manufacturing

  3. Material Acquisition / Processing

  4. Transport

  5. End of Life

Impact and interpretation will be briefly covered under the document’s motivation and use cases sections.

There is reason to suspect that nebulous definitions combined with the competitive pressure might produce greenwashing. Greenwashing involves making an unsubstantiated claim to deceive consumers into believing that a vendor’s product or solution is environmentally friendly or has a greater positive environmental impact than it does. This document proposes the following initiative to counter these effects.

1.1. The Sustainability Telemetry Standard Specification

As an industry, we need to cooperate and agree on a set of core KPIs that are measured, including the definition of terms, units, and measurement procedures. What is included, and what is not included.

Sustainability metrics require a broad diversity of data sources that need to be combined.

  • Static information. Data coming from manufacturing, including reference values on how the assets have been designed if they enable reuse and recycling, and which materials have been used during manufacturing and packaging; normally this information is defined once and it is part of data sheets provided by the vendors.

  • Dynamic data. Information measured in real-time or close to real-time from the networking equipment or application. For instance, metrics should consider current inventory and current source and amount of consumed power, as well as what hardware and software features are enabled and used by the specific network equipment.

  • Best practices. Recommendations for optimizing the use of the network equipment, throughout its complete lifecycle.

  • Local context. Country-specific regulations, corporate policy, and social aspects.

To enable the exchange of sustainability data among all interested parties, deployment considerations that are out of the scope of this document will need to include:

  • Data models. The model definition can be implemented in different forms. This document proposes YANG as part of the Specification Data Model. YANG can be used independently of the transport and can be converted into any encoding format supported by the network configuration protocol. YANG models are decoupled from the management protocol layer.

  • Sustainability framework. To drive adoption, we propose an open-source aggregation framework for sustainability data. This framework should be seen as a reference architecture for a sustainability monitoring mechanism. While each implementation may be (and will be) different, the basic framework shall remain constant. The framework must account for vendor-specific calculations and enhancements in a plug-in architecture.

YANG data models as part of the Sustainability Telemetry Specification, which will follow this document, have been classified as follows:

  • Identification of the assets. Assets include hardware (physical as well as virtual), software, applications, and services. The asset concept is defined in the Data Model for Lifecycle Management and Operations (DMLMO) [I-D.draft-palmero-ivy-dmalmo-01] IETF draft.

  • Power and Efficiency. To measure power consumption and energy efficiency, common methods, attributes, and units are needed to define metrics. The approach needs to cover the different networking domains, starting with hardware focus, but including software and protocols attributes and metrics.

  • Circular Economy attributes. Collecting circularity data (such as materials used, or the embedded emissions footprint) is expensive and difficult because of confidentiality and the non-standardized approach to reporting and exchanging circularity data. The flow of circularity data is typically lost at each step throughout the supply chain, as goods are passed through suppliers, manufacturers, system integrators, distributors, customers, and consumers into reuse and recycling.

  • Context metrics. Without understanding the context of the use, none of the metrics listed above will provide much value. The carbon intensity of the power used, for example, is key to assessing the sustainability of a given application. An efficiency number needs to be interpreted differently at peak hours and night. A given usage may be considered less sustainable if someone demonstrates the ability to deliver the same end-user value with a smaller footprint. A system that is transported a shorter distance or using a more sustainable mode of transportation from the factory to the installation site may also be assessed more positively. Or if it has a longer economic life or comes with less single-use packaging.

The model definition can be implemented in different forms. We would like to propose a specific YANG model for the sustainability metrics, which intrinsically allows for a variety of collection protocols. YANG can be used independently of the transport protocol, and lends itself well to be converted into a variety of encoding formats supported by popular network configuration protocols.

The rest of this document is organized as follows. Section 2 establishes the terminology and abbreviations. Section 3 outlines the goals and motivation of Sustainability metrics. Section 4 discusses Use Cases that lay out the groundwork for the Sustainability Telemetry Specification, to address new business needs introduced by the Circular Economy and to avoid excessive climate change. Section 5 proposes principles for and a solution based on existing standards and current internet drafts. Section 6 lists ideas for future development of this work.

1.2. Requirements language

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.

2. Terminology

Terminology and abbreviations used in this document:

Hardware, software, applications, or services. An asset can be physical or virtual.
Marketing (intentionally or not) an asset as being green (i.e. fitting well into the circular economy) by selectively omitting less green aspects of the asset.
Circular economy
An economic paradigm in which the full lifecycle cost of resource use and emissions are included.
Climate change
The disruption of ecological processes caused by excessive resource use or emissions.

3. Motivation

Aside from the need for consistency on metrics to be considered as part of the ICT sector, to reduce environmental impact and increase benefit; this document and future work related, aim to support the Digital Product Passport initiative under the European Union’s (EU’s) Circular Economy Action Plan (CEAP) and the Ecodesign for Sustainable Products Regulation (ESPR). There is not much time for businesses to prepare and for IETF work to influence this development.

The Digital Product Passport (DPP) is key to the EU’s transition to a circular economy and will provide information about assets’ environmental sustainability. It aims to improve traceability and transparency along the entire value chain of an asset and to improve the management and sharing of product-related data which are critical to ensuring their sustainable use, prolonged life, and circularity.

There is a need to:

In the case of upgrading, repairing, repurposing, or remanufacturing a product, it should be clear the responsibility to update the information is transferred to the installer, repairer, or remanufacturer who will be putting the product into service or placing it on the EU market.

The three main target groups of the passport are:

The DPP will help business planners and consumers make informed choices when purchasing assets, and should also help local and public authorities to better perform checks and controls.

4. Use Cases

4.1. Use Case I

4.1.1. Scenario ‘monitoring power’

An organization is running a large and complex network with many types of devices. By looking at the utility bills, it is clear that the organization is consuming rather more energy per transported bit than many other organizations. Exactly which devices or network functions are at the root of the situation is unclear, however.

The product LCA in this scenario applies to the stage of “Use”.

4.1.2. Sustainability Insights Added Value

By providing near-real-time data that is broken down at least to an individual hardware device, and ideally considerably deeper than that, it will be possible to attribute energy and environmental footprint costs to different device types, service types, and individual customers.

If one customer is altering its behavior or load on the network, a monitoring application could detect this quickly. It would also be possible to try several implementations or configurations for a given service and get quick feedback on the operations cost of that change.

4.2. Use Case II

4.2.1. Scenario ‘migration’

An organization is running a network with a variety of managed services and applications. Some of the devices are getting old, and have lower energy efficiency than more modern devices. Replacing old devices with new ones might improve efficiency, but has an economical as well as environmental cost. Without specific performance data, it is difficult to make informed decisions about upgrades.

The product LCA stage applies to “Use”.

4.2.2. Sustainability Insights Added Value

By providing KPIs for reading sustainability parameters that pertain to actual usage, rather than numbers from data sheets, the accuracy of upgrade decisions is enhanced. Such data can make the case for an upgrade very clear and easy to make, or it may show that it’s not a good idea at this time. In both cases improving the sustainability of the operations.

4.3. Use Case III

4.3.1. Scenario ‘recycling’

Recycling and reuse are major drivers of the circular economy. Companies must put high efforts in this direction and transparency. This is a qualitative KPI, passed if percentages of recycled and reused goods given the manufacturing options, as well as reports listing how many units have been recycled.

The product LCA applies to the stage of “Material Acquisition / Processing”.

4.3.2. Sustainability Insights Added Value

The trend seems to be to report on the percentage of recycled user devices and the eco-design and refurbishment efforts. Sustainability Insights can enable the data sources to report comprehensive reporting of recycling efforts.

4.4. Use Case IV

4.4.1. Scenario ‘power optimization’

An organization is running a network with a variety of managed services and applications. The network and application performance is continuously monitored, and there are even some automatic remediation actions that may trigger when certain conditions are detected.

In this scenario, the product LCA applies to the stage of “Use”.

4.4.2. Sustainability Insights Added Value

By providing KPIs for sustainability parameters such as power consumption and power efficiency, the monitoring system can access relevant data and perform actions that reduce the power consumption or sustainability footprint of the delivered services.

For example, some overlay redundant links or systems may be powered off at non-pick hours, or enter into a low-power mode. A highly available application may be configured to take more load in the data center with a lower price of energy, lower outside temperatures, or an environmentally superior energy mix.

4.5. Use Case V

4.5.1. Scenario ‘sustainability cost’

IT solutions are currently analyzed from two main perspectives: technological and economical. When looking at environmental, social, and corporate governance (ESG) impact topics, sustainability metrics in the context of digital transformation, deliver insights into opportunities and risks that emerge from a rapidly growing stakeholder demand for sustainable, digitally advanced products and services.

The product LCA applies to all stages under its lifecycle.

4.5.2. Sustainability Insights Added Value

From an application point of view, this use case proposes to include Sustainability factors in the Total Cost of Ownership (TCO) calculation, where there is a need to add Environmental, Social, and Governmental Key Performance Indicators (KPIs) to the analysis. However, adding Sustainability metrics comes with challenges and trades-off. Future work considers a model to calculate the Total Sustainability Cost of Ownership (TSCO) for network solutions based on the ESG Materiality Matrix. This model is open to adding any implementation that takes into consideration Sustainability objectives at a point in time, but it also evolves with the needs of the business and the stakeholders. The initial scope proposes to investigate the top four most important ESG Materiality issues as a base to grow the TCO to a TSCO that matches the Company’s priorities and issues.

Future work might include use cases that will cover “Manufacturing”, “Transport” and “End of Life” examples.

4.6. Use Case VI

4.6.1. Scenario ‘switch off’

WIFI is deployed in any famous stadiums around the world. It is common for such networks to rely on several thousands of Access Points (APs).

Such networks are very dense and designed to separate finely fans connections from business operations (ticketing, …).

Such WIFI network activity varies in time and follow a well-known calendaring (but not as trivial as the match calendar). It is obvious that the bigger part of a stadium WIFI network footprint is most of the time unused at all.

Current WIFI management tools are not designed to stop and restart the APs automatically following a schedule. In 2022 Orange and Cisco implemented practical actions to lower the power consumption of the WIFI APs of the Orange velodrome of Marseille. The experimentation was able to save 20% of the APs power consumption without modifying the infrastructure. The experimentation shown that the current design of network operation tools need to be updated to save up to 50%.

Stadium and arenas WIFI network are made of a very limited number of clusters. There are WIFI networks similar in size which are by far more constraint in term of calendaring and capillarity. In France, banks have thousands of branches. See Number of bank branches in France.

NB: APs are not per designed build to support numerous cold restarts. This may impacts TSCO

4.6.2. Sustainability Insights Added Value

Being able to stop and restart WIFI APs with the right time, space and service granularity.

From an operation point of view, this use case proposes to save power consumption during periods the APs are not in-used.

5. Proposals for a Solution

5.1. Basic Principles

5.1.1. Describe the Collected Data using YANG

There are many data/info modeling languages out there, but we are not aware of any that comes close to YANG in its ability to concretely and extensibly describe data structures and with a tools eco system to render functionality from that.

5.1.2. Work with Existing Equipment

Network devices as well as servers and cooling systems are providing metrics in various formats today. We could standardize how they should report their information, and require them to follow certain principles for accounting. We could then watch as these new systems spread across the industry. Optimistically, this would take a few years to standardize and another few to be implemented in devices. Then yet another few years before it is widely deployed in production networks. These are years that must not be lost in our efforts to achieve reasonably sustainable networks. Therefore, we propose that focusing on retrieving the data we need from the systems out there, without inventing new reporting APIs/mechanisms on the equipment level. The interfaces on existing devices are sometimes YANG-based (e.g. YANG-push), but most are probably not (e.g. CLI scraping, SNMP, homegrown REST, …)

5.1.3. New Systems that want to be Helpful

While we can’t expect all systems out there to adopt any new APIs or reporting mechanisms, some system implementors may be interested to help out as much as possible. In such cases, we think the best thing a system implementor can do is to provide a catalog of the existing sensors/measurement points the system provides, along with all the metadata that the higher layers will need. We can standardize what such an (optional) catalog would look like. This will make it easier to integrate with the rest of the metrics collection system. Implementing it may give a system a competitive advantage over systems that don’t.

5.1.4. Time Series Database (TSDB) Storage

The already established industry norm seems to be storing any collected and aggregated telemetry data in a Time Series Database (TSDB).

5.1.5. Define Telemetry Component Roles: Providers, Collectors, Aggregators, Processors

We need to agree on some terms. We have used the term “provider” to mean any system that is reporting (sustainability) telemetry. This could be a router, blade server or a building’s cooling system.

The telemetry data flow is initiated by a “collector”. The aim of the collector is to ensure that data from a provider ends up in a time series database, along with relevant metadata. How it accomplishes this is outside the scope our efforts (could use e.g. polling, subscriptions, YANG-push, …).

“Aggregators” and “processors” take telemetry data flow(s) from a time series database and apply some sort transformation/aggregation operation on them, and deliver the result to a time series database. That could very well be a different partition/table/bucket in the same time series database.

5.1.6. Add YANG and Metadata where missing, and keep the Metadata with the Data

There are of course plenty of systems and telemetry data flows that has no YANG description, but that can be solved. We, who are building the telemetry stack, can add YANG descriptions to any data flow we care about, if the system implementor doesn’t. Similarly, aggregators and processors describe their outgoing data flows using YANG models.

Both the YANG descriptions of the data flows and the corresponding metadata descriptions of the flows should be kept close to the data it pertains to. Preferably in or linked to the same database.

5.1.7. Transparency: Solid YANG to Time Series Database (TSDB) Mapping

Since all the data stored in the TSDB has a YANG description, we can define an algorithm that maps any YANG defined data to the flat, tagged, naming structure suitable for use in a TSDB. This makes it easy to trace the origins of all the data in the TSDB. It is key to be able to answer questions about the origins of the collected data without looking into code. All the sources, and which processing steps are applied, should be visible and editable as configuration data, not deeply hidden in code created elsewhere.

5.2. The Sustainability Framework

To drive quick adoption, we propose to build an open-source aggregation framework for sustainability data. This framework should be seen as a reference architecture for a sustainability monitoring mechanism. The reference implementation will be based on the specifications mentioned in this document. The architecture would supply a few base components, but otherwise, allow vendors or standards bodies to plugin their applications that fit in the general framework.

One example of such an application that we would like to propose is a model to calculate the Total Sustainability Cost of Ownership (TSCO) for network solutions based on the Environmental, Social, and Governance (ESG) Materiality Matrix. This matrix model is open to adding any implementation that takes into consideration Sustainability objectives at a point in time, but it also evolves with the needs of the business and the stakeholders. The initial scope proposes to investigate the top four most important ESG Materiality issues as a base to grow the TCO to a TSCO that matches the Company’s priorities and issues.

5.3. The Framework Architecture

Even though the end-goal with the architecture is to enable fully automated network management which is taking the sustainability insights that comes out of the network into account, we envision the top layer of the architecture would also contain a human consumable graphs showing how well our network is operating with respect to energy and emissions. We live in a demo-or-die world, after all. Such graphs typically take their input from one or more Time-Series Databases (TSDB). The mapping from YANG to TSDB is described in [I-D.draft-kll-yang-label-tsdb-00]. The graphing technology already exists, and many mature commercial and open source tools are available. This operator user interface would also do well to have a section for recommendations from the system, based on potential savings the system has discovered.

An aggregation framework that collects information about, and to a large extent directly from, the network elements and subsystems in the network, and delivers it into a TSDB. Sometimes, the best available data about some of the properties of some nework elements is found in the datasheets for the network element. In such cases, the information about a network element may be read from a web server or file with structured information about the network element, rather than reading it in (near) real-time from the system itself. We propose that the aggregation system is built on the framework described in the [I-D.draft-lindblad-tlm-philatelist-01].

In the figure below, many lines are drawn between components in the aggregation framework. In general, those lines represent telemetry data flows in and out of one or more TSDB buckets/topics. This state of affairs is depicted in the diagram with a TSDB symbol “hanging in the air” next to these lines.

Besides aggregating the collected telemetry data, the Philatelist framework can also be used to associate selected points in the collection tree with specific assets defined in the network inventory, as defined in [I-D.draft-palmero-ivy-dmalmo-01].

Lower down in the stack, the aggregation framework needs to access the actual devices, or other data sources standing in for them. The variety in types of sensors and collection protocols to use here is great. It is hard to know what sensors are available from a device, and using which protocols. It is even harder to know more about what the delivered metrics mean. What unit of measuremnt is used? Is the power reported true RMS power, or something else? What is included and not in this number, e.g. is the cooling cost included? What is the precision? This metadata information SHOULD come from the device vendor, either declared directly by the device itself, or by providing a structured data manifest. The YANG interface and file format for the manifest is described in [I-D.draft-opsawg-poweff-01].

A concrete device YANG model that provides some functionality for reading power telemetry as well as some power control functionality on the device level is found in [I-D.draft-li-ivy-power-01].

                      | USER INTERFACE  |       ________
                      |      Graphs     |      /        \
                      |                 |     (   TSDB   )
                      +-----------------+     |\________/|
                               |              |          |
                              ...              \________/
       |               |               |              |
+------------+  +------------+  +------------+  +------------+
| Normalizer |  |  Network   |  |  Storage   |  |  Compute   |
+------------+  +------------+  +------------+  +------------+
       |           |                   |\             |\
      ...         ...                  ...            ...
       |           |                   |              |
       |- YANG     |-YANG              |-YANG         |-YANG
       |- Metadata |-Metadata          |-Metadata     |-Metadata
       |           |                   |              |
+------------+  +------------+  +------------+  +------------+
+------------+  +------------+  +------------+  +------------+
       |           |                   |\             |\
      ...         ...                  ...            ...
       |           |                   |              |
       +- YANG     |                   +- some YANG   +- YANG
       +- Metadata +- Metadata         +- Metadata    +- Metadata
       |           |                   |              |
+----------+  +-----------+  +--------------+  +----------------+
| NETCONF  |  | CLI       |  | Device       |  | Device with    |
| device   |  | and SNMP  |  | with REDFISH |  | POWEFF models  |
|          |  | device    |  | and RESTCONF |  | over NETCONF   |
+----------+  +-----------+  +--------------+  +----------------+

Figure 1: Example component diagram sketch of a Sustainability Insights deployment.

6. Next Steps

To enable the exchange of sustainability data among all interested parties at each step of the value supply chain, a technical sustainability framework for how this data is queried, transported, and visualized will be required.

6.1. Reaching Methodological Agreement

6.1.1. Need to Agree on What and How to Measure

Once we agree on the principles of how a collection framework should be structured, we also need to agree on what data is meaningful to measure from the systems, and how that measurement is done. What units to convert to, what sample frequencies and how (im)precision is conveyed. What about missing data points?

6.1.2. Need to Agree on What and How to Aggregate

Once we agree on what and how to measure, we also need to agree on how we aggregate the data. Is linear interpolation of time series data a good approach? Should we work with averages, and if so over which time spans? Filter out outliers? How deep into the device subsystems/interfaces etc should we drill? How should we add cost of cooling, buildings, operations teams, etc?

6.1.3. Let’s bring in the Economists from SBTi, GHGP, etc.

On the e-impact mailing list, we have identified a number of hairy questions when it comes to attribution/allocation of energy or co2eq-cost, that are more of policy nature than hard science. E.g. who is “responsible” for the idle power consumption of a system? Does it matter if users pay a flat monthly fee for services, or per byte or per cat video? All the above is about computing a cost-metric for running the system. It may be equally important to compute a value metric (e.g. based on what users pay for the service) to put in relation to the cost. Just cost alone will be pretty much a useless number.

To sort out questions on this level, it may be wise to involve “the economists”. By that we mean the folks that are already assessing many of our organizations when it comes to co2-reduction targets etc. Folks from Science Based Targets initiative (SBTi), or Green House Gas Protocol (GHGP), for example. They are used to construct economic models where the right incentives show up.

6.2. Expanding the Scope

Items that are not in the scope of this edition of this document, but could be addressed in future revisions, include:

  • How to relate Sustainability Telemetry Specification to sustainability Scopes 1, 2, and 3,

  • Circular Economy Business models,

  • Recommendations,

  • Scope 4, i.e. metrics for avoided footprints (sometimes called handprint). For instance, to reduce GHG emissions, automation activities like Zero Touch, or certain technologies like Routed Optical Networking, can replace other higher emitting activities. Another example would be the positive impact arising from video conferencing as opposed to international travel by airplane.

7. Security Considerations

The security considerations mentioned in section 17 of [RFC7950] apply.

Sustainability Insights brings several security and privacy implications because of the various components and attributes of the information model. For example, each functional component can be tampered with to give manipulated data. Sustainability Insights when used alone or with other relevant data, can identify an individual, revealing Personal Identifiable Information (PII). Misconfigurations can lead to data being accessed by unauthorized entities.

Methods exist to secure the communication of management information. The transport entity of the functional model MUST implement methods for secure transport. This document also contains an Information model and Data-Model in which none of the objects defined are writable. If the objects are deemed sensitive in a particular environment, access to them MUST be restricted using appropriately configured security and access control rights. The information model contains several optional elements which can be enabled or disabled for the sake of privacy and security. Proper authentication and audit trail MUST be included for all the users/processes that access Sustainability Insights Telemetry Data.

8. References

8.1. Normative References

Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, , <>.
Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, , <>.

8.2. Informative References

Larsson, K., "Mapping YANG Data to Label-Set Time Series", Work in Progress, Internet-Draft, draft-kll-yang-label-tsdb-00, , <>.
Li, T. and R. Bonica, "A YANG model for Power Management", Work in Progress, Internet-Draft, draft-li-ivy-power-01, , <>.
Lindblad, J., "Philatelist, YANG-based Network Controller collection and aggregation framework integrating Telemetry data and Time Series Databases", Work in Progress, Internet-Draft, draft-lindblad-tlm-philatelist-01, , <>.
Lindblad, J., Mitrovic, S., Palmero, M., and G. Salgueiro, "Power and Energy Efficiency", Work in Progress, Internet-Draft, draft-opsawg-poweff-01, , <>.
Palmero, M., Brockners, F., Kumar, S., Cardona, C., and D. Lopez, "Data Model for Asset Lifecycle Management and Operations", Work in Progress, Internet-Draft, draft-palmero-ivy-dmalmo-01, , <>.
Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language", RFC 7950, DOI 10.17487/RFC7950, , <>.

Change log

RFC Editor Note: This section is to be removed during the final publication of the document.


This document was created by meaningful contributions from Jeff Apcar, Klaus Verschure and Suresh Krishnan.

The authors wish to thank them and many others for their helpful comments and suggestions.

Authors' Addresses

Per Andersson
Cisco Systems
Jan Lindblad
Cisco Systems
Snezana Mitrovic
Cisco Systems
Marisol Palmero
Cisco Systems
Esther Roure
Cisco Systems
Gonzalo Salgueiro
Cisco Systems
Emile Stephan