面向地理分布式数据中心生命周期碳排放降低的老化感知在线分布式调度
Aging-Aware Online Distributed Scheduling for Lifecycle Carbon Reduction in Geo-Distributed Data Centers
- Guangzhou University（广州大学）
- The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
- Shenzhen Research Institute of Big Data（深圳大数据研究院）
- Rausser College of Natural Resources, UC Berkeley（加州大学伯克利分校罗斯尔自然资源学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对地理分布式数据中心，提出一种老化感知的在线分布式调度框架，结合指数老化模型和增强Lyapunov及ZSP-ADMM方法，在保护隐私的同时降低生命周期碳排放和运营成本。
AI中文摘要：
由云计算和人工智能（AI）驱动的数据中心（DC）的快速普及，导致了巨大的能源需求和碳排放，带来了显著的可持续性挑战。针对地理分布式数据中心的碳感知优化已被广泛研究。大多数现有方法主要关注服务器使用产生的运营碳排放。然而，现有文献常常忽略工作负载引起的热应力，这会加速非线性硬件老化。这导致服务器更换更加频繁，最终增加隐含碳排放。为解决这些局限性，我们提出了一个针对分布式数据中心的综合碳生命周期建模框架。除了运营碳排放外，这项工作将工作负载调度与基于利用率的指数老化模型相结合，以评估服务器老化带来的长期碳成本。为了以在线且保护隐私的方式求解所提出的优化模型，首先引入了一种具有时变队列偏移（TVQS）的增强型Lyapunov框架来处理系统不确定性。然后，开发了一种基于零和扰动的交替方向乘子法（ZSP-ADMM）框架，以实现跨地理分离的数据中心的分布式协调，同时保护本地交换的工作负载信息。仿真结果表明，与基准方法相比，所提出的方法实现了高达13.0%的碳排放降低和12.6%的运营成本降低。
英文摘要：
The rapid proliferation of data centers (DCs), driven by cloud computing and artificial intelligence (AI), has led to massive energy demand and carbon emissions, posing significant sustainability challenges. Carbon-aware optimization in geographically distributed data centers has been widely studied. Most existing approaches mainly focus on operational carbon emissions from server usage. However, existing literature often ignores workload-induced thermal stress, which accelerates nonlinear hardware degradation. This leads to more frequent server replacements and ultimately increases embodied carbon emissions. To address these limitations, we propose a comprehensive carbon life-cycle modeling framework for distributed data centers. Apart from operational carbon emissions, this work combines workload scheduling with a utilization-dependent exponential aging model to evaluate long-term carbon costs from server degradation. In order to solve the proposed optimization model in an online and privacy-preserving manner, an enhanced Lyapunov framework with time-varying queue shifting (TVQS) is first introduced to handle system uncertainties. Then, a zero-sum perturbation-based alternating direction method of multipliers (ZSP-ADMM) framework is developed to enable distributed coordination across geographically separated data centers while protecting locally exchanged workload information. Simulation results demonstrate that the proposed approach achieves up to 13.0% lower carbon emissions and 12.6% lower operational costs compared with benchmarks.
