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Data-driven Algorithm Selection for Carbon-Aware Scheduling

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Názov: Data-driven Algorithm Selection for Carbon-Aware Scheduling
Autori: Roozbeh Bostandoost, Walid A. Hanafy, Adam Lechowicz, Noman Bashir, Prashant Shenoy, Mohammad Hajiesmaili
Zdroj: ACM SIGEnergy Energy Informatics Review. 4:148-153
Informácie o vydavateľovi: Association for Computing Machinery (ACM), 2024.
Rok vydania: 2024
Popis: As computing demand continues to grow, minimizing its environmental impact has become crucial. This paper presents a study on carbon-aware scheduling algorithms, focusing on reducing carbon emissions of delay-tolerant batch workloads. Inspired by the Follow the Leader strategy, we introduce a simple yet efficient meta-algorithm, called FTL, that dynamically selects the most efficient scheduling algorithm based on real-time data and historical performance. Without fine-tuning and parameter optimization, FTL adapts to variability in job lengths, carbon intensity forecasts, and regional energy characteristics, consistently outperforming traditional carbon-aware scheduling algorithms. Through extensive experiments using real-world data traces, FTL achieves 8.2% and 14% improvement in average carbon footprint reduction over the closest runner-up algorithm and the carbon-agnostic algorithm, respectively, demonstrating its efficacy in minimizing carbon emissions across multiple geographical regions. 1
Druh dokumentu: Article
Jazyk: English
ISSN: 2770-5331
DOI: 10.1145/3727200.3727222
Rights: URL: https://www.acm.org/publications/policies/copyright_policy#Background
Prístupové číslo: edsair.doi...........f457cfa4f8ef3a3749a2d1f8ffe4fb11
Databáza: OpenAIRE
Popis
Abstrakt:As computing demand continues to grow, minimizing its environmental impact has become crucial. This paper presents a study on carbon-aware scheduling algorithms, focusing on reducing carbon emissions of delay-tolerant batch workloads. Inspired by the Follow the Leader strategy, we introduce a simple yet efficient meta-algorithm, called FTL, that dynamically selects the most efficient scheduling algorithm based on real-time data and historical performance. Without fine-tuning and parameter optimization, FTL adapts to variability in job lengths, carbon intensity forecasts, and regional energy characteristics, consistently outperforming traditional carbon-aware scheduling algorithms. Through extensive experiments using real-world data traces, FTL achieves 8.2% and 14% improvement in average carbon footprint reduction over the closest runner-up algorithm and the carbon-agnostic algorithm, respectively, demonstrating its efficacy in minimizing carbon emissions across multiple geographical regions. 1
ISSN:27705331
DOI:10.1145/3727200.3727222