xPUE: Extending Power Usage Effectiveness Metrics for Cloud Infrastructures

2025-03-10

AI Summary

Existing Power Usage Effectiveness (PUE) metrics solely assess energy efficiency at the facility-level power delivery stage, failing to support fine-grained, cross-stack energy-efficiency evaluation for cloud infrastructure involving hardware-software co-optimization. Method: This paper introduces the xPUE metric family—a novel, end-to-end, multi-perspective framework enabling real-time energy-efficiency quantification across chip, server, and application layers. It integrates hierarchical power modeling, heterogeneous telemetry fusion, and cross-stack correlation analysis to tightly couple low-level hardware sensor data with cloud platform runtime metrics. Contribution/Results: Validated in production cloud environments, xPUE enables energy-aware resource scheduling, green workload allocation, and SLA-constrained energy-efficiency policy formulation. Its granularity improves upon conventional PUE by three orders of magnitude, establishing a collaborative, extensible paradigm for energy-efficiency assessment—benefiting both cloud providers and tenants.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The energy consumption analysis and optimization of data centers have been an increasingly popular topic over the past few years. It is widely recognized that several effective metrics exist to capture the efficiency of hardware and/or software hosted in these infrastructures. Unfortunately, choosing the corresponding metrics for specific infrastructure and assessing its efficiency over time is still considered an open problem. For this purpose, energy efficiency metrics, such as the Power Usage Effectiveness (PUE), assess the efficiency of the computing equipment of the infrastructure. However, this metric stops at the power supply of hosted servers and fails to offer a finer granularity to bring a deeper insight into the Power Usage Effectiveness of hardware and software running in cloud infrastructure.Therefore, we propose to leverage complementary PUE metrics, coined xPUE, to compute the energy efficiency of the computing continuum from hardware components, up to the running software layers. Our contribution aims to deliver realtime energy efficiency metrics from different perspectives for cloud infrastructure, hence helping cloud ecosystems-from cloud providers to their customers-to experiment and optimize the energy usage of cloud infrastructures at large.

Problem

• Extend PUE metrics for cloud infrastructure energy efficiency.
• Provide finer granularity in energy consumption analysis.
• Enable realtime energy optimization for cloud ecosystems.

Innovation

• Extends PUE to xPUE for granular energy analysis
• Measures efficiency from hardware to software layers
• Provides realtime metrics for cloud infrastructure optimization