CarbonClipper: Optimal Algorithms for Carbon-Aware Spatiotemporal Workload Management
<|reference_start|>CarbonClipper: Optimal Algorithms for Carbon-Aware Spatiotemporal Workload Management: 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.<|reference_end|>
@article{lechowicz2024carbonclipper:,
    title={CarbonClipper: Optimal Algorithms for Carbon-Aware Spatiotemporal
  Workload Management},
    author={Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad
  Hajiesmaili, Adam Wierman, Prashant Shenoy},
    journal={arXiv preprint arXiv:2408.07831},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07831},
    primaryClass={cs.DS cs.DC cs.LG}
}