The rapid expansion of data centers (DCs) has intensified energy and carbon footprint, incurring a massive environmental computing cost. While carbon-aware workload migration strategies have been examined, existing approaches often overlook reliability metrics such as server lifetime degradation, and quality-of-service (QoS) that substantially affects both carbon and operational efficiency of DCs. Hence, this paper proposes a comprehensive optimization framework for spatio-temporal workload migration across distributed DCs that jointly minimizes operational and embodied carbon emissions while complying with service-level agreements (SLA). A key contribution is the development of an embodied carbon emission model based on servers' expected lifetime analysis, which explicitly considers server heterogeneity resulting from aging and utilization conditions. These issues are accommodated using new server dispatch strategies, and backup resource allocation model, accounting hardware, software and workload-induced failure. The overall model is formulated as a mixed-integer optimization problem with multiple linearization techniques to ensure computational tractability. Numerical case studies demonstrate that the proposed method reduces total carbon emissions by up to 21%, offering a pragmatic approach to sustainable DC operations.

翻译：数据中心的快速扩张加剧了能源消耗与碳足迹，带来了巨大的环境计算成本。尽管已有研究探讨了碳感知的工作负载迁移策略，但现有方法往往忽略了服务器寿命衰减等可靠性指标，以及显著影响数据中心碳排放和运行效率的服务质量。因此，本文提出一个面向分布式数据中心时空工作负载迁移的综合优化框架，该框架在满足服务等级协议的同时，联合最小化运行碳排放和隐含碳排放。一个关键贡献是基于服务器预期寿命分析建立了隐含碳排放模型，该模型明确考虑了由老化和使用状况导致的服务器异构性。这些问题通过新的服务器调度策略和备用资源分配模型得以处理，同时考虑了硬件、软件及工作负载引发的故障。整体模型被表述为一个混合整数优化问题，并采用多种线性化技术以确保计算可处理性。数值案例研究表明，所提方法可将总碳排放降低高达21%，为可持续数据中心运营提供了一种实用方法。