Technology Category
Machine Learning: Hardware-aware MLPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Algorithm Configuration
MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions
🤖 AI Summary
The high energy consumption of cloud computing intensifies carbon emission pressures, with data centers accounting for 2–4% of global electricity use; ICT sector electricity demand may rise to 40% by 2040. Private clouds—constituting 87% of organizational cloud usage—suffer from coarse-grained carbon management and insufficient cross-domain coordination.
Method: We propose a carbon-aware dynamic resource ranking algorithm that jointly integrates real-time and forecasted grid carbon intensity, Power Usage Effectiveness (PUE), fine-grained energy monitoring, and deep hypervisor-level virtualization integration to enable granular, energy-efficient scheduling across multi-cloud and edge environments.
Contribution/Results: Our method directly optimizes workload placement via hypervisor-level intervention. Evaluated on a real-world distributed data center infrastructure, it achieves an 85.68% reduction in CO₂ emissions, significantly enhancing the co-optimization of climate performance and operational efficiency. The approach demonstrates strong scalability and engineering deployability.
Technology Category
Application Category
📝 Abstract
Cloud computing drives innovation but also poses significant environmental challenges due to its high-energy consumption and carbon emissions. Data centers account for 2-4% of global energy usage, and the ICT sector's share of electricity consumption is projected to reach 40% by 2040. As the goal of achieving net-zero emissions by 2050 becomes increasingly urgent, there is a growing need for more efficient and transparent solutions, particularly for private cloud infrastructures, which are utilized by 87% of organizations, despite the dominance of public-cloud systems.
This study evaluates the MAIZX framework, designed to optimize cloud operations and reduce carbon footprint by dynamically ranking resources, including data centers, edge computing nodes, and multi-cloud environments, based on real-time and forecasted carbon intensity, Power Usage Effectiveness (PUE), and energy consumption. Leveraging a flexible ranking algorithm, MAIZX achieved an 85.68% reduction in CO2 emissions compared to baseline hypervisor operations. Tested across geographically distributed data centers, the framework demonstrates scalability and effectiveness, directly interfacing with hypervisors to optimize workloads in private, hybrid, and multi-cloud environments. MAIZX integrates real-time data on carbon intensity, power consumption, and carbon footprint, as well as forecasted values, into cloud management, providing a robust tool for enhancing climate performance potential while maintaining operational efficiency.
Problem
Research questions and friction points this paper is trying to address.
Optimize cloud operations to reduce carbon emissions
Rank resources dynamically for energy efficiency
Integrate real-time carbon data into cloud management
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic ranking of resources by carbon intensity
Real-time and forecasted carbon data integration
Hypervisor interfacing for workload optimization
🔎 Similar Papers
No similar papers found.
