YANG Data Model for Energy Measurements in Streaming Devices

Internet-Draft	Energy YANG Model for Streaming	February 2025
Dom, et al.	Expires 28 August 2025	[Page]

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.¶

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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.¶

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.¶

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

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:¶

{
  "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.¶
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.¶

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.¶