DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing
Abstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-03-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-021-00437-7 |
| _version_ | 1818610484764475392 |
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| author | Hossein Ahmadvand Fouzhan Foroutan Mahmood Fathy |
| author_facet | Hossein Ahmadvand Fouzhan Foroutan Mahmood Fathy |
| author_sort | Hossein Ahmadvand |
| collection | DOAJ |
| description | Abstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption. |
| first_indexed | 2024-12-16T15:15:10Z |
| format | Article |
| id | doaj.art-8268a117c417441fae8dc35558d4f3f9 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-16T15:15:10Z |
| publishDate | 2021-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-8268a117c417441fae8dc35558d4f3f92022-12-21T22:26:50ZengSpringerOpenJournal of Big Data2196-11152021-03-018111610.1186/s40537-021-00437-7DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processingHossein Ahmadvand0Fouzhan Foroutan1Mahmood Fathy2Sharif University of TechnologySharif University of TechnologyIran University of Science and Technology and School of Computer Science, Institute for Research in Fundamental SciencesAbstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.https://doi.org/10.1186/s40537-021-00437-7Data varietyDVFSEnergy consumptionBig Data |
| spellingShingle | Hossein Ahmadvand Fouzhan Foroutan Mahmood Fathy DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing Journal of Big Data Data variety DVFS Energy consumption Big Data |
| title | DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing |
| title_full | DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing |
| title_fullStr | DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing |
| title_full_unstemmed | DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing |
| title_short | DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing |
| title_sort | dv dvfs merging data variety and dvfs technique to manage the energy consumption of big data processing |
| topic | Data variety DVFS Energy consumption Big Data |
| url | https://doi.org/10.1186/s40537-021-00437-7 |
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