We study carbon-aware spatiotemporal workload management, which seeks to address the growing environmental impact of data centers. We formalize this as an online problem called spatiotemporal online allocation with deadline constraints ($\mathsf{SOAD}$), in which an online player completes a workload (e.g., a batch compute job) by moving and scheduling the workload across a network subject to a deadline $T$. At each time step, a service cost function is revealed, representing, e.g., the carbon intensity of servicing a workload at each location, and the player must irrevocably decide the current allocation. Furthermore, whenever the player moves the allocation, it incurs a movement cost defined by a metric space $(X,d)$ that captures, e.g., the overhead of migrating a compute job. $\mathsf{SOAD}$ formalizes the open problem of combining general metrics and deadline constraints in the online algorithms literature, unifying problems such as metrical task systems and online search. We propose a competitive algorithm for $\mathsf{SOAD}$ along with a matching lower bound that proves it is optimal. Our main algorithm, ${\rm C{\scriptsize ARBON}C{\scriptsize LIPPER}}$, is a learning-augmented algorithm that takes advantage of predictions (e.g., carbon intensity forecasts) and achieves an optimal consistency-robustness trade-off. We evaluate our proposed algorithms for carbon-aware spatiotemporal workload management on a simulated global data center network, showing that ${\rm C{\scriptsize ARBON}C{\scriptsize LIPPER}}$ significantly improves performance compared to baseline methods and delivers meaningful carbon reductions.

翻译：本文研究碳感知时空工作负载管理，旨在应对数据中心日益增长的环境影响。我们将该问题形式化为一种带截止时间约束的时空在线分配问题（$\mathsf{SOAD}$），其中在线参与者需通过在网络中迁移和调度工作负载（例如批量计算任务）并在截止时间 $T$ 前完成。每个时间步会揭示一个服务成本函数（例如各位置处理工作负载的碳强度），参与者必须不可撤销地确定当前分配方案。此外，每当参与者迁移分配时，将产生由度量空间 $(X,d)$ 定义的运动成本（例如迁移计算任务的开销）。$\mathsf{SOAD}$ 形式化了在线算法领域中结合通用度量与截止时间约束的开放问题，统一了度量任务系统与在线搜索等问题。我们为 $\mathsf{SOAD}$ 提出了一种竞争算法及匹配的下界证明其最优性。主要算法 ${\rm C{\scriptsize ARBON}C{\scriptsize LIPPER}}$ 是一种学习增强算法，可利用预测信息（如碳强度预报）实现最优的一致性-鲁棒性权衡。我们在模拟的全球数据中心网络上评估了所提出的碳感知时空工作负载管理算法，结果表明 ${\rm C{\scriptsize ARBON}C{\scriptsize LIPPER}}$ 相比基线方法显著提升性能，并实现显著的碳减排效果。