Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints
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3726854.3727292.pdf
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Author(s)
Lechowicz, Adam
Christianson, Nicolas
Sun, Bo
Bashir, Noman
Hajiesmaili, Mohammad
Wierman, Adam
Shenoy, Prashant
Date Issued
June 9, 2025
Publisher
ACM|Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems
Citation
dam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, and Prashant Shenoy. 2025. Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints. In Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS '25). Association for Computing Machinery, New York, NY, USA, 169–171.
Version
Final published version
Abstract
We introduce and study spatiotemporal online allocation with deadline constraints (SOAD), a new online problem motivated by emerging challenges in sustainability and energy. In SOAD, an online player completes a workload by allocating and scheduling it on the points of a metric space (X, d) while subject to a deadline T. At each time step, a service cost function is revealed that represents the cost of servicing the workload at each point, and the player must irrevocably decide the current allocation of work to points. Whenever the player moves this allocation, they incur a movement cost defined by the distance metric d(•, •) that captures, e.g., an overhead cost. 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 SOAD along with a matching lower bound establishing its optimality. Our main algorithm, ST-CLIP, is a learning-augmented algorithm that takes advantage of predictions (e.g., forecasts of relevant costs) and achieves an optimal consistency-robustness trade-off. We evaluate our proposed algorithms in a simulated case study of carbon-aware spatiotemporal workload management, an application in sustainable computing that schedules a delay-tolerant batch compute job on a distributed network of data centers. In these experiments, we show that ST-CLIP substantially improves on heuristic baseline methods.
Description
SIGMETRICS Abstracts ’25, Stony Brook, NY, USA
MIT Department
MIT Office of Sustainability
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3726854.3727292
