MAIZX动态优化云资源调度，显著降低碳排放。
MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions
- 根据实时与预测的碳强度、能效等指标动态排序云资源。
- 相比基线虚拟机操作，碳排放降低85.68%。
- 适合关注低碳运营的私有云与混合云部署者。
云计算推动创新，但高能耗和碳排放带来严峻环境挑战。数据中心占全球能源消耗的2-4%，到2040年信息技术行业用电量占比预计达40%。尽管公共云占主导，仍有87%的组织使用私有云。本研究评估了MAIZX框架，通过实时与预测的碳强度、电源使用效率（PUE）及能耗，动态优化数据中心、边缘节点与多云环境的资源调度。该框架采用灵活的排序算法，在地理分布的数据中心中实现规模化有效运行，直接对接虚拟机管理程序，优化私有、混合及多云工作负载。结果表明，相较基线虚拟机操作，碳排放减少85.68%。MAIZX整合实时与预测的碳强度、能耗与碳足迹数据，为提升云环境气候表现提供有力工具，同时保障运行效率。
原文摘要 · Abstract (English)
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.
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