Network Working Group D. Dom
Internet-Draft id3as
Intended status: Standards Track B. Benjamin
Expires: 28 August 2025 CTOiC
C. Christoph
Broadpeak
24 February 2025
YANG Data Model for Energy Measurements in Streaming Devices
draft-neumann-green-streaming-yang-00
Abstract
This document defines a YANG data model for representing energy
measurements from streaming-capable devices. The model supports both
instantaneous power readings and correlated streaming metrics needed
to analyze energy efficiency in streaming applications.
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.
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
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and may be updated, replaced, or obsoleted by other documents at any
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This Internet-Draft will expire on 28 August 2025.
Copyright Notice
Copyright (c) 2025 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. YANG Model Structure . . . . . . . . . . . . . . . . . . . . 3
2.1. Energy Measurement Data . . . . . . . . . . . . . . . . . 3
2.2. Device Identification and Capabilities . . . . . . . . . 3
2.3. Streaming-related Metrics . . . . . . . . . . . . . . . . 3
3. YANG Module . . . . . . . . . . . . . . . . . . . . . . . . . 3
4. JSON Encoding . . . . . . . . . . . . . . . . . . . . . . . . 4
5. Data Model Details . . . . . . . . . . . . . . . . . . . . . 5
5.1. Device Information . . . . . . . . . . . . . . . . . . . 5
5.2. Measurement Capabilities . . . . . . . . . . . . . . . . 6
5.3. Power Readings . . . . . . . . . . . . . . . . . . . . . 6
5.4. Streaming Metrics . . . . . . . . . . . . . . . . . . . . 7
6. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 7
7. Normative References . . . . . . . . . . . . . . . . . . . . 7
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction
The increasing focus on environmental sustainability in streaming
media delivery requires accurate measurement and analysis of energy
consumption across the delivery chain. This document defines a YANG
data model for reporting energy measurements from streaming-capable
devices, along with relevant streaming metrics needed for correlation
and analysis.
The model is designed to support both A-side (mains power) and B-side
(internal component) power measurements while maintaining clear
distinction between these measurement types. The internal component
usage may be used by a collecting entity to estimate the total device
power used if such power measurements are not available within the
device.
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2. YANG Model Structure
The proposed YANG model defines a comprehensive framework for
representing energy measurements in streaming devices. The model is
structured around four key components that together provide a
complete picture of device energy consumption and its relationship to
streaming activities.
2.1. Energy Measurement Data
The core of the model focuses on both instantaneous and historical
power measurements. This dual approach allows for real-time
monitoring as well as trend analysis. The model distinguishes
between total system power (A-side) and component-level power
(B-side) measurements to support different measurement capabilities
across device types.
2.2. Device Identification and Capabilities
Proper device identification and capability reporting is important
for accurate data interpretation. This component allows devices to
clearly communicate their measurement capabilities, ensuring that
data consumers can properly interpret and validate the reported
measurements.
2.3. Streaming-related Metrics
To enable meaningful energy efficiency analysis, the model includes
streaming-specific metrics that can be correlated with power
measurements. These metrics provide essential context for
understanding energy consumption patterns in relation to actual
streaming activities.
3. YANG Module
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module: eyang
+--rw energy-measurements
+--rw device-info
| +--rw device-id string
| +--rw device-type enumeration
| +--rw measurement-capabilities
| +--rw supports-mains-power? boolean
| +--rw supports-component-power? boolean
| +--rw supports-component-usage? boolean
+--rw power-readings
| +--rw timestamp yang:timestamp
| +--rw mains-power
| | +--rw watts? decimal64
| +--rw component-power
| | +--rw cpu-watts? decimal64
| | +--rw gpu-watts? decimal64
| | +--rw memory-watts? decimal64
| | +--rw network-watts? decimal64
| +--rw component-usage
| +--rw cpu-usage? decimal64
| +--rw gpu-usage? decimal64
| +--rw memory-usage? decimal64
| +--rw network-usage? decimal64
+--rw streaming-metrics
+--rw active-streams? uint32
+--rw total-bandwidth-mbps? decimal64
+--rw video-brightness? uint8
+--rw encoding-parameters? string
+--rw network-utilization? decimal64
4. JSON Encoding
The following shows the JSON schema for data exchange:
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{
"energy-measurements": {
"device-info": {
"device-id": "string",
"device-type": "string",
"measurement-capabilities": {
"supports-mains-power": boolean,
"supports-component-power": boolean
"supports-component-usage": boolean
}
},
"power-readings": {
"timestamp": "string",
"mains-power": {
"watts": number,
},
"component-power": {
"cpu-watts": number,
"gpu-watts": number,
"memory-watts": number,
"network-watts": number
},
"component-usage": {
"cpu-usage": number,
"gpu-usage": number,
"memory-usage": number,
"network-usage": number
}
},
"streaming-metrics": {
"active-streams": number,
"total-bandwidth-mbps": number,
"video-brightness": number,
"encoding-parameters": "string",
"network-utilization": number
}
}
}
5. Data Model Details
5.1. Device Information
* device-id: Opaque identifier for the device. Device
identification is important for proper data attribution and
analysis.
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* device-type: The device-type enumeration is chosen to cover the
major categories of streaming infrastructure while remaining
extensible for future devices. Each device type may have
different measurement capabilities and characteristics that affect
how their energy data should be interpreted. One of: encoder,
decoder, cdn-cache, router, switch, settop-box, tv, mobile-device
5.2. Measurement Capabilities
The capability reporting mechanism allows devices to explicitly
declare their measurement abilities. This is essential because:
* Not all devices can measure both mains and component power
* Measurement methods may vary in accuracy and reliability
* Some devices may only support subset of measurements
This information helps data consumers properly interpret and validate
the reported measurements. The following attributes indicates which
types of power measurements the device supports:
* supports-mains-power: Device can report total power draw
* supports-component-power: Device can report component-level power
* supports-component-usage: Device can report component-level usage
5.3. Power Readings
Power readings are structured to support both high-level and detailed
energy analysis:
* timestamp: UTC timestamp of measurement. Timestamp precision is
critical for correlation analysis
* mains-power: Total power draw from mains (A-side measurement).
Mains power provides system-level energy perspective
* component-power: Individual component power measurements (B-side).
Component power enables detailed efficiency analysis
* component-usage: Individual component measurements usage (B-side).
Component usage enables estimating the power used if such power
measurements are not available within the device.
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5.4. Streaming Metrics
The streaming metrics are selected to enable use-case that require
correlation analyses:
* active-streams: Number of active streaming sessions as an
indicator for system load
* total-bandwidth-mbps: Current bandwidth utilization as an
indicator for network utilization
* video-brightness: Average screen brightness (0-255)
* encoding-parameters: Active encoding settings that may affect
processing requirements
* network-utilization: Network interface utilization (%)
These metrics were chosen based on empirical research showing their
relevance to energy consumption patterns.
6. Acknowledgement
This draft has been elaborated in the context of the working group 7
of Greening of Streaming.
7. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
Authors' Addresses
Dom Robinson
id3as
20 Rowan Way
Brighton, Sussex
United Kingdom
Email: dom@id3as.co.uk
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Benjamin Schwarz
CTOiC
89, rue des Poissonniers
Paris
France
Email: bs@ctoic.net
Christoph Neumann
Broadpeak
15, rue Claude Chappe
35510 Cesson-Sevigne
France
Email: christoph.neumann@broadpeak.tv
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