GMC: Greening MapReduce Clusters Considering Both Computational Energy and Cooling Energy
| Autor: | Mohammad M. R. Lunar, Novia Nurain, A. S. M. Rizvi, A. B. M. Alim Al Islam, Tarik Reza Toha |
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| Rok vydání: | 2018 |
| Předmět: |
Consumption (economics)
020203 distributed computing business.industry Computer science Distributed computing Testbed Cloud computing 02 engineering and technology Energy consumption Power (physics) chemistry.chemical_compound chemistry 020204 information systems Carbon dioxide 0202 electrical engineering electronic engineering information engineering Cluster (physics) Cooling energy Electricity business Energy (signal processing) |
| Zdroj: | ICC |
| DOI: | 10.1109/icc.2018.8422113 |
| Popis: | Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this paper, we propose a machine learning based approach named as Green MapReduce Cluster (GMC) that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. GMC predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of GMC over a real testbed. Our evaluation reveals that GMC reduces total energy consumption by up to 47% compared to other alternatives while experiencing marginal throughput degradation in a few cases. |
| Databáze: | OpenAIRE |
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