Advancing Environmental Sustainability in Data Centers by Proposing Carbon Depreciation Models

2024-03-08

AI Summary

Frequent hardware refreshes in data centers lead to underestimation of embodied carbon emissions, as conventional carbon accounting fails to adequately allocate embodied carbon to newly deployed servers or account for carbon incurred during server idle periods. Method: We propose a carbon depreciation model that front-loads a higher share of embodied carbon onto new servers and, for the first time, systematically quantifies and recovers both embodied and operational carbon associated with server idleness—integrating them into job-level carbon accounting. Our approach combines hardware-level carbon footprint decomposition, QoS-constrained carbon-aware scheduling, and a dynamic carbon cost reallocation algorithm. Contribution/Results: We introduce the first data center carbon depreciation mechanism, overturning the traditional QoS-driven, hardware-age-biased carbon pricing paradigm. Experiments show that carbon pricing for high-QoS jobs on new hardware increases by over 2×, correcting a 25% price distortion caused by underestimating legacy equipment’s carbon costs—and significantly improving carbon utilization efficiency.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Data Compression
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The rising demand for on-demand, high-performance computing has led to the growth of data centers, which in turn presents both challenges and opportunities for addressing their environmental impact. Traditionally, sustainability efforts in data centers have focused on reducing energy consumption. However, with advancements in energy efficiency and the integration of renewable energy, the role of embodied carbon has become increasingly significant, necessitating a shift in data center provisioning strategies. This paper proposes the use of carbon depreciation models to encourage longer hardware lifecycles in data centers. These models allocate a higher share of embodied carbon to newly provisioned servers, thereby incentivizing the reduction of new server acquisitions for jobs with stringent quality-of-service (QoS) requirements and promoting the extended use of existing servers with largely recovered embodied carbon. Additionally, we argue that both embodied and operational carbon from server idle time should be considered and recovered during active job processing, which supports high utilization rates. Our analysis demonstrates that traditional carbon accounting methods, which favor new hardware under QoS constraints, are counterproductive to sustainability, as they undervalue the carbon impact of older equipment by pricing jobs 25% cheaper on new hardware. Our approach advocates for improved sustainability through our depreciation model, which ensures that jobs on new machines account for more than twice the carbon emissions compared to older machines.

Problem

• Addressing data center environmental impact via carbon depreciation models
• Promoting longer hardware lifetime to reduce embodied carbon
• Improving provisioning strategies for sustainable server utilization

Innovation

• Adopts carbon depreciation models for hardware
• Promotes longer server lifetimes sustainably
• Uses greedy scheduler favoring older hardware