Internet-Draft Green Networking Metrics July 2022
Clemm, et al. Expires 12 January 2023 [Page]
Network Working Group
Intended Status:
A. Clemm
L. Dong
G. Mirsky
L. Ciavaglia
Rakuten Mobile
J. Tantsura
M-P. Odini

Green Networking Metrics


This document explains the need for network instrumentation that allows to assess the power consumption, energy efficiency, and carbon footprint associated with a network, its equipment, and the services that are provided over it. It also suggests a set of related metrics that, when provided visibility into, can help to optimize a network's energy efficiency and "greenness".

Status of This Memo

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This Internet-Draft will expire on 12 January 2023.

Table of Contents

1. Introduction

Climate change and the need to curb greenhouse emissions have been recognized by the United Nations and by most governments as one of the big challenges of our time. As a result, improving energy efficiency and reducing power consumption are becoming of increasing importance for society and for many industries. The networking industry is no exception.

Networks themselves consume significant amounts of energy. Therefore, the networking industry has an important role to play in meeting sustainability goals. Future networking advances will increasingly need to focus on becoming more energy-efficient and reducing carbon footprint, both for economic reasons and for reasons of corporate responsibility. This shift has already begun and sustainability is already becoming an important concern for network providers [telefonica2020].

There are many vectors along which networks can be made "greener". At its foundation, it involves network equipment itself. Making such equipment more energy-efficient is a big factor in helping networks become greener. However, opportunities also exist at the level of protocols themselves (e.g. reduction of transmission waste and enabling of rapid control loops), at the level of the overall network (e.g. path optimization under consideration of energy efficiency as a cost factor), and architecture level (e.g. placement of contents and functions) [I.D.draft-cwx-green-ps].

However, regardless of any particular approach that is chosen, in order to assess its impact, there is a need to have visibility into the actual energy consumption that is occurring and to ideally be able to attribute that consumption to its sources. As the adage goes, you cannot manage what you cannot measure. By extension, you cannot optimize what you have no visibility of. The ability to instrument networks in a way that allows for the assessment of energy consumption is hence an important enabler for potential energy optimizations, allowing to assess the effectiveness of measures that are being taken and enabling (for example) control loops that involve energy as an input. Before instrumenting, it needs to be clear, however, what the proper metrics are that network providers will be interested in and that applications will seek to optimize.

This document defines a set of metrics that allow to assess the "greenness" of networks and that form the basis for optimizing energy efficiency, carbon footprint, and environmental sustainability of networks and the services provided. These metrics are intended to serve the foundation for possible later IETF standardization activities, such as the definition of related YANG modules [RFC7950] or energy-related control protocol extensions.

Please note that throughout this document, we will be using the terms "green" and "energy efficient" interchangeably. In general, we will be use these terms in a broad sense, encompassing also carbon footprint and sustainability except when explicitly mentioned otherwise. Likewise, we treat "energy efficiency" as synonymous with "energy utilization efficiency", broadly speaking referring to the efficiency with which energy is being utilized.

2. Definitions and Acronyms

3. Energy Metrics

In the following, we categorize energy metrics as follows:

3.1.1. Base Metrics

Arguably the most relevant energy metrics relate to equipment as a whole. After all, power is drawn from devices.

The power consumption of the device can be divided into the consumption of the core components (e.g. the backplane and CPU) as well as additional consumption incurred per port and line card. In [I.D.draft-manral-bmwg-power-usage], the device factors affecting power consumption are summarized: base chassis power, number of line cards, number of active ports, port settings, port utilization, implementation of packet classification of Ternary Content-Addressable Memory (TCAM) and the size of TCAM, firmware version.

Furthermore it is important to understand the difference between power consumption when a resource is idling versus when it is under load. This helps to understand the incremental cost of additional transmission versus the initial cost of transmission. Generally, the cost of the first bit could be considered very high, as it requires powering up a device, port, etc. The cost of transmission of additional bits (beyond the first) is many orders of magnitude lower. Likewise, the incremental cost of CPU and memory that will be needed to process additional packets becomes fairly negligible.

The first set of metrics corresponds to ratings of the device:

  • Power consumption when idle (e.g. Watts)
  • Power consumption when fully loaded (e.g. Watts)
  • Power consumption at various loads: e.g. 50% utilization, 90% utilization

These metrics should be maintained for the device as a whole, and for the subcomponents: i.e. for the chassis by itself, for each line card, for each port. It should also take into account aspects such as the current memory configuration, as the overall energy consumption of a device is a function of the energy consumption of the components the system is comprised of.

The metrics could be provided by the data sheet associated with the device or they could be measured as part of a deployment. For maximum accuracy and comparability, they should reflect pre-defined environmental setting, e.g., operating temperature, relative humidity, barometric pressure. For example, ATIS (Alliance for Telecommunications Industry Solutions) [ATIS0600015.02] defines a reference environment under which to measure router power consumption: temperature of 25 celsius degree (within 3 celsius degree deviation), relative humidity of 30% to 75%, barometric pressure between 1020 and 812 mbar. In the AC power configuration, the router should be evaluated at 230 VAC or within 1% deviation, 50 or 60 Hz or within 1% deviation [Ahn2014].

The second set of metrics reflects the actual power being drawn during operation. It is the type of data that might be provided as management data. Again, it should be provided for the device as a whole, as well as for the subcomponents reflected in the device hierarchy: line cards, ports, etc.

  • Current power consumption (e.g. Watts)
  • Power drawn since system start (or module insertion, if at the level of a line card, or port activation, if at the level of a port), for the past minute (e.g. Watt hours)

The third set of metrics are derived from the earlier metrics. They normalize the power consumption relative to the line speeds respectively amount of traffic that is passed.

  • Current power consumption / kilooctet

The fourth set of metrics reflects expectation values about incremental energy usage. It could be relevant for use cases that assess the cost of additional traffic. [Bolla2011] and [Ahn2014] found that the power consumption of a router is in direct proportion of the link utilization as well as the packet sizes.

  • Incremental power per packet, per kilooctet, per gigaoctet. (Possible units might be pWh - pico Watt hours)

In addition to these metrics, it is conceivable to also have the device reflect other context of relevance, such as the sustainability rating of the power source. This could potentially be reflected along a scale ranging from diesel-generator powered, via conventional power grid, to renewable (powered by windmill, capture of excess heat, etc). Also, the environmental status of the device could be taken into consideration, such as whether it is deployed in a data center and its share in contributing to the need for cooling. It is conceivable to, for example, introduce corresponding metrics indicating a "green rating" of device, and/or of the context in which a device has been deployed.

3.1.2. Virtualization Considerations

Instrumentation should also take into account the possibility of virtualization. This is important in particular as networking functions may increasingly be virtualized and hosted (for example) in a data center. Overlay networks may be formed. Likewise, many applications expected to optimize energy consumption may be hosted on controllers and applied to soft switches, VNFs (Virtual Network Functions), or networking slices. The attribution of actual power consumed to such virtualized entities is a non-trivial task. It involves navigating layers of indirection to assess actual energy usage and contribution by individual entities. While it would be possible in such cases to simply revert to energy metrics of CPUs and data centers as a whole, this loses the ability to account for those metrics on the basis of networking decisions being made.

For example, virtualized networking functions could be hosted on containers or virtual machines which are hosted on a CPU in a data center instead of a regular network appliance such as a router or a switch, leading to very different power consumption characteristics. A data center CPU could be more power efficient and consume power more proportionally to actual CPU load. Virtualization could result in using fewer servers. [Energystar] reports that one watt-hour of energy savings at the server level results in roughly 1.9 watt-hours of facility-level energy savings by reducing energy waste in the power infrastructure and reducing energy needed to cool the waste heat produced by the server.

Instrumentation needs to reflect these facts and facilitate attributing power consumption in a correct manner. Alternatively, a simpler solution may be to simply forgo energy metrics for virtualized functions entirely, instead focus on instrumenting and relying on optimizing the energy footprint of the underlying hosting infrastructure. In the meantime, the attribution of energy consumption and carbon footprint to individual functions that run on top of that infrastructure may be a topic for further research.

Energy metrics related to flows attempt to capture the contribution of a given flow to energy consumption. In its basic incarnation, those metrics reflect the energy consumption at a given device. They could be used in conjunction with IPFIX [RFC7011] and modeled as Information Elements to be treated analogous to other flow statistics [RFC7012]. The following is a corresponding set of flow energy metrics:

A second set of energy metrics related to flow might aggregate the flow's energy consumption over the entire flow path. In that case, the flow energy consumption is added up along the systems of the traversed path. In practice, this will be more difficult to assess for many reasons, including impacts of load balancing, PREOF (Packet Replication, Elimination, and Ordering Functions [RFC8655]), challenges to trace actual routes taken by production traffic, and more.

Enerby metrics related to paths involve assessing the carbon footprints of paths and optimizing those paths so that overall footprint is minimized, then applying techniques such as path-aware networking [I.D.draft-chunduri-rtgwg-preferred-path-routing] or segment routing [RFC8402] to steer traffic along those paths that are deemed "the greenest" among alternatives. It also includes aspects such as considering the incremental energy usage in routing decisions.

Optimizing cost has a long tradition in networking; many of the existing mechanisms can be leveraged for greener networking simply by introducing energy footprint as a cost factor. Low-hanging fruit include the inclusion of energy-related parameters as a cost parameter in control planes, whether distributed (e.g. IGP) or conceptually centralized via SDN controllers. In addition to power consumption over a path itself, other factors such as paths involving intermediate routers that are powered by renewable energy resources might be considered, as might be determined by an aggregate sustainability score. After all, paths with devices that are powered by solar, wind, or geothermal might be preferable over paths involving devices powered by conventional energy that may include fossil fuel or nuclear resources.

The following are a corresponding set of candidate metrics:

Ultimately, the goal of energy optimization and reduction of carbon footprint is to minimize the aggregate amount of energy used across the entire network, as well as to minimize the overall carbon footprint of the network as a whole. Accordingly, metrics that aggregate the energy usage across the network as a whole are needed. In order to account for changing traffic profiles, growth in user traffic etc, additional metrics are needed that normalize the total over the volume of services supported and volume of traffic passed. Corresponding metrics will generally be computed at the level of Operational Support Systems (or Business Support Systems) for the entire network.

Some of the metrics used include the following [telefonica2020]:

4. Other considerations and discussion items

This document is intended to spark discussion about what energy metrics will be useful to reduce the carbon footprint of networks - that provide visibility into energy consumption, that help optimization of networks under green criteria, that enable the next generation of energy-aware controllers and services. Clearly, other metrics are conceivable and more considerations apply beyond those that are currently reflected in this document. The following subsections highlight items that warrant further discussion and that might be addressed in greater detail in future revisions of this document.

4.1. User perspective

Arguably, attributing energy usage to individual users and making users aware of the energy-implications of their communication behavior may provide interesting possibilities to reduce energy footprint by guiding their behavior accordingly. For example, the network could present clients with energy statistics related to their communication usage. This could be supported by metrics related to service instances, such as energy usage statistics beyond statistics regarding volume, duration, number of transactions. Such approaches would raise questions about how to actually collect such statistics accurately (versus just computing them via a formula) or what to actually include as part of those statistics (amortized vs incuremental energy contribution, attribution of cost for path resilience or retransmissions due to congestion, etc). They also raise questions about how they would in practice be used. For example, energy-based charging might be explored as an alternative for volume-based charging; however, in practice the two may be strongly correlated and rejected by customers for similar reasons that volume-based charging is frequently rejected.

4.2. Holistic perspective

The network itself is only one contributor to a network's carbon footprint. Arguably just as important are aspects outside the network itself, such as cooling and ventilation. These aspects need to be considered. However, reflecting such aspects here would arguably result in "boiling the ocean" and are therefore not addressed here.

4.3. Sustainable equipment production

Internet energy consumption may constitute two major components [Raghavan2011]: (1) the energy of the devices that construct the Internet, including the infrastructure devices: routers, LAN devices, cellular and telecommunication infrastructure, (2) More broadly, with the rise of peer-to-peer applications and cloud services, it also considers the energy consumption of the end systems, including desktops, laptops, smart phones, cloud servers, and application servers that are not in the cloud.

For those two components, the following factors need to take into consideration for energy consumption calculation:

By combining the energy consumption for running each device that builds the Internet [JuniperRouterPower], and the energy consumption of the end systems, in the meantime counting the energy consumption of manufacturing, operational maintenance, replacement and lifespan, disposal of those devices and equipment, we may have an estimate of the energy consumption for the network as a whole.

5. IANA Considerations

This document does not have any IANA requests.

6. Security Considerations

When instrumenting a network for energy metrics, it is important that implementations are secured to ensure that data is accurately measured and cannot be tampered with. For example, an attacker might try to tamper energy readings to confuse controller trying to minize power consumption, leading to increased power consumption instead. In addition, access to the data needs to be secured in similar ways as for other sensitive management data, for example using secure management protocols and subjecting energy data that is maintained in YANG datastores via NACM (NETCONF Access Control Model).

However, it should be noted that this draft specifies only metrics themselves, not how to instrument networks accordingly. For the definition of metrics themselves, security considerations do thus not really apply.

7. Acknowledgments

Acknowledgments will be added when the time comes.

8. Informative References

Ahn, J. and H. S. Park, "Measurement and modeling the power consumption of router interface", DOI: 10.1109/ICACT.2014.6779082, 16th International Conference on Advanced Communication Technology, pp. 860-863, , <>.
AITS, "Energy Efficiency for Telecommunication Equipment: Methodology for Measurement and Reporting - Transport and Optical Access Requirements", .
Bolla, R., Bruschi, R., Lombardo, C., and D. Suino, "Evaluating the energy-awareness of future Internet devices", DOI: 10.1109/HPSR.2011.5986001, 2011 IEEE 12th International Conference on High Performance Switching and Routing, pp. 36-43, , <>.
EnergyStar, "12 Ways to Save Energy in the Data Center, Server Virtualization", , <>.
Bryant, S. E., Chunduri, U., and A. Clemm, "Preferred Path Routing Framework", , <>.
Clemm, A. and C. Westphal, "Challenges and Opportunities in Green Networking", .
Manral, V., "Benchmarking Power usage of networking devices", .
Juniper, "Power Requirements for an MX960 Router", .
Raghavan, B. and J. Ma, "The energy and emergy of the Internet", HotNets-X: Proceedings of the 10th ACM Workshop on Hot Topics in Networks, pp. 1-6, , <>.
(Ed.), B. C., (Ed.), B. T., and P. Aitken, "Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of Flow Information", RFC 7011, , <>.
(Ed.), B. C. and B. T. (Ed.), "Information Model for IP Flow Information Export (IPFIX)", RFC 7012, , <>.
Bjorklund, M. E., "The YANG 1.1 Data Modeling Language", RFC 7950, , <>.
(Ed.), C. F., (Ed.), S. P., Ginsberg, L., Decraene, B., Decraene, B., Litkowski, S., and R. Shakir, "Segment Routing Architecture", RFC 8402, , <>.
Finn, N., Thubert, P., Varga, B., and J. Farkas, "Deterministic Networking Architecture", RFC 8655, , <>.
Telefonica, "Telefonica Consolidated Annual Report 2020.", .

Authors' Addresses

Alexander Clemm
2220 Central Expressway
Santa Clara, CA 95050
United States of America
Lijun Dong
2220 Central Expressway
Santa Clara, CA 95050
United States of America
Greg Mirsky
Laurent Ciavaglia
Rakuten Mobile
Jeff Tantsura
Marie-Paule Odini