Opportunistic Scheduling in Clouds Partially Powered by Green Energy

The fast growth of demand for computing and storage resources in data centers has considerably increased their energy consumption. Improving the utilization of data center resources and integrating renewable energy, such as solar and wind, has been proposed to reduce both the brown energy consumptio...

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Bibliographic Details
Published in2015 IEEE International Conference on Data Science and Data Intensive Systems pp. 448 - 455
Main Authors Yunbo Li, Orgerie, Anne-Cecile, Menaud, Jean-Marc
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Biological system modeling
Cloud computing
Energy consumption
Optimization
Random access memory
Renewable energy sources
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Summary:The fast growth of demand for computing and storage resources in data centers has considerably increased their energy consumption. Improving the utilization of data center resources and integrating renewable energy, such as solar and wind, has been proposed to reduce both the brown energy consumption and carbon footprint of the data centers. In this paper, we propose a novel framework oPportunistic schedulIng broKer infrAstructure (PIKA) to save energy in small mono-site data centers. In order to reduce the brown energy consumption, PIKA integrates resource overcommit techniques that help to minimize the number of powered-on Physical Machines (PMs). On the other hand, PIKA dynamically schedules the jobs and adjusts the number of powered-on PMs to match the variable renewable energy supply. Our simulations with a real-world job workload and solar power traces demonstrate that PIKA saves brown energy consumption by up to 44.9% compared to a typical scheduling algorithm.
DOI:10.1109/DSDIS.2015.80