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LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand
April 24, 2024, 4:42 a.m. | Roozbeh Bostandoost, Adam Lechowicz, Walid A. Hanafy, Noman Bashir, Prashant Shenoy, Mohammad Hajiesmaili
cs.LG updates on arXiv.org arxiv.org
Abstract: Motivated by an imperative to reduce the carbon emissions of cloud data centers, this paper studies the online carbon-aware resource scaling problem with unknown job lengths (OCSU) and applies it to carbon-aware resource scaling for executing computing workloads. The task is to dynamically scale resources (e.g., the number of servers) assigned to a job of unknown length such that it is completed before a deadline, with the objective of reducing the carbon emissions of executing …
abstract algorithms arxiv carbon cloud cloud data computing cs.dc cs.lg data data centers demand emissions job paper reduce resources scale scaling studies type uncertain workloads
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