E2E AI INFERENCE ENERGY MONITORING
E2E energy monitoring for Al inference in mobile networks
This paper introduces a comprehensive framework for end-to-end energy monitoring of AI inference in mobile networks. It highlights that overlooked network energy consumption during data transport can be as significant as AI model inference itself. Utilizing a cross-layer and in-band telemetry approach, the framework quantifies energy usage across network functions and AI models. Experimental results demonstrate that network energy can be on par with or significantly contribute to total energy consumption and associated CO2 emissions, especially for simpler AI tasks or when using energy-efficient models. The findings underscore the critical need to integrate network energy considerations into sustainable AI system design for a truly green AI future.
Key Executive Impact
Discover the crucial metrics demonstrating the real-world implications of energy-aware AI deployment in mobile networks.
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Potential Energy Savings
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Network Energy Share (Simple Tasks)
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Network CO2 Contribution (Simple Tasks)
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Operational Efficiency Boost
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
System Overview
The paper outlines an end-to-end energy monitoring framework for AI inference in mobile networks. It focuses on Vision-Language Models (VLMs) and the energy consumed both by the AI models during inference and by the mobile network for transporting contextual data from far-edge devices to the AI models. This addresses a critical gap where network energy consumption is often overlooked.
Monitoring Methodology
The framework utilizes In-Situ Operations, Administration, and Maintenance (IOAM) for application data path tracing, embedding TraceID and NodeID in IPv6 packets. This allows correlation of packet data with specific applications and network functions. Energy measurements for Radio Units (RUs) are done via metered Power Distribution Units (PDUs), while Virtual Network Functions (VNFs) like DU, CU, and UPF, along with AI model inference, are monitored using Kepler within Kubernetes. Energy attribution is calculated considering static and dynamic components based on packet proportion and data rates.
Key Findings
Experiments on a private 5G network reveal that for simpler AI tasks, the energy consumed by the 5G network can be on par with that of energy-efficient AI models (e.g., Qwen2.5 models). For more complex tasks, network energy is typically 20-30% of the VLM's consumption for larger models. Furthermore, network-induced CO2 emissions are substantial, ranging from 70% to 89% of total emissions for simple tasks and 60% to 84% for complex tasks, even when data centers use renewable energy. Optimizing model choice and data rates can lead to up to 68% energy reduction.
Strategic Implications
The findings emphasize the critical need to incorporate network energy consumption into sustainable AI system design. Ignoring network energy leads to an incomplete and potentially misleading assessment of AI's environmental footprint. Companies deploying AI in mobile-edge environments must consider end-to-end energy monitoring and carbon-aware network operations to achieve true sustainability and optimize operational costs.
89%
Network’s Share of Total CO2 Emissions (Simple Tasks)
For simple AI tasks, the mobile network can be responsible for up to 89% of total CO2 emissions, even if the AI model runs on renewable energy. This highlights a significant, often overlooked, contributor to AI's carbon footprint.
E2E Energy Monitoring Process
Far-Edge Context Generation
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Data Transport (Mobile Network)
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IOAM Telemetry & Energy Monitoring
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AI Inference & Energy Monitoring
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Application-Level Energy Estimation
| Factor | Simple Tasks | Complex Tasks |
|---|---|---|
| Network Energy vs. VLM | On par with energy-efficient VLMs (e.g., Qwen2.5) | 20-30% of VLM energy (for larger models) |
| Total Energy Reduction Potential | Up to 68% with optimized model/rate | Negligible for larger models |
| Network CO2 Contribution | 70-89% of total emissions | 60-84% of total emissions |
Smart Factory AI Monitoring Use Case
Description: A robot with a camera monitors an assembly line, sending video streams to a VLM for analysis (e.g., object identification, anomaly detection).
Challenge: Traditionally, only VLM inference energy is considered, neglecting the energy for transporting high-volume video data over the 5G network. Multiple applications and varying QoS requirements make network energy attribution challenging.
Solution: The proposed E2E framework uses IOAM to trace video stream paths through 5G NFs and Kepler to monitor VNF and VLM energy. This provides a holistic view of energy consumption.
Impact: Identified that network energy consumption can significantly contribute to the total, sometimes equaling or exceeding VLM inference energy. This enables informed decisions for optimizing both AI model and network configurations for sustainability.
Calculate Your Potential ROI
Estimate the financial and operational benefits of implementing energy-aware AI solutions in your enterprise.
Annual Cost Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
Our proven phased approach ensures a smooth and effective integration of sustainable AI into your operations.
Phase 01: Discovery & Assessment
Comprehensive analysis of existing AI workflows, network infrastructure, and energy consumption patterns to identify key optimization opportunities.
Phase 02: Framework Integration
Deployment and customization of the E2E energy monitoring framework, including IOAM telemetry and Kepler integration, tailored to your specific environment.
Phase 03: Data Collection & Analysis
Gathering real-time energy and carbon data for AI inference and network transport. Detailed analysis to pinpoint inefficiencies and quantify impact.
Phase 04: Optimization & Strategy
Development and implementation of strategies for AI model selection, data rate optimization, and network configuration to minimize energy and carbon footprint.
Phase 05: Continuous Monitoring & Refinement
Ongoing monitoring and performance tuning to ensure sustained energy efficiency and adaptability to evolving AI workloads and network conditions.
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