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Hossein Ahmadvand
Sharif University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0003-1121-1914
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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 from previous work. 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.
Keywords
Data Variety, DVFS, Energy Consumption, Big Data
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Editorial decision: Accept
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Received 10 Jan, 2021
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Received 30 Nov, 2020
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Received 15 Aug, 2020
Reviewer #10 agreed
On 13 Aug, 2020
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Received 13 Aug, 2020
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Received 09 Aug, 2020
Reviewer #9 agreed
On 08 Aug, 2020
Reviewer #8 agreed
On 06 Aug, 2020
Review #8 received
Received 06 Aug, 2020
Review #7 received
Received 06 Aug, 2020
Review #3 received
Received 04 Aug, 2020
Reviewer #4 agreed
On 04 Aug, 2020
Reviewer #5 agreed
On 04 Aug, 2020
Reviewer #6 agreed
On 04 Aug, 2020
Reviewer #7 agreed
On 04 Aug, 2020
Reviewer #1 agreed
On 03 Aug, 2020
Reviewer #2 agreed
On 03 Aug, 2020
Reviewer #3 agreed
On 03 Aug, 2020
Review #1 received
Received 03 Aug, 2020
Review #2 received
Received 03 Aug, 2020
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A new preprint design is coming!
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See the published version of this preprint at Journal of Big Data.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Hossein Ahmadvand
Sharif University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0003-1121-1914
Badges
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Complete author information
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Peer Review Timeline
CURRENT STATUS: Published
View the published article at Journal of Big Data.
No community comments so far
Editorial decision: Accept
On 01 Mar, 2021
Reviewer #2 agreed
On 10 Jan, 2021
Review #1 received
Received 10 Jan, 2021
Reviewer #1 agreed
On 04 Jan, 2021
Reviewers invited
Invitations sent on 03 Jan, 2021
Editor assigned
On 29 Dec, 2020
Submission checks complete
On 29 Dec, 2020
Editor invited
On 29 Dec, 2020
Version 3
Posted 04 Dec, 2020
No comments provided
Editorial decision: Minor Revision
On 04 Dec, 2020
Review #2 received
Received 03 Dec, 2020
Review #1 received
Received 30 Nov, 2020
Reviewer #2 agreed
On 27 Nov, 2020
Reviewer #1 agreed
On 26 Nov, 2020
Reviewers invited
Invitations sent on 25 Nov, 2020
Editor assigned
On 21 Nov, 2020
Submission checks complete
On 21 Nov, 2020
Editor invited
On 21 Nov, 2020
No comments provided
Editorial decision: Minor Revision
On 22 Oct, 2020
Review #5 received
Received 16 Oct, 2020
Review #4 received
Received 06 Oct, 2020
Reviewer #5 agreed
On 05 Oct, 2020
Review #3 received
Received 01 Oct, 2020
Review #2 received
Received 01 Oct, 2020
Review #1 received
Received 29 Sep, 2020
Reviewer #3 agreed
On 28 Sep, 2020
Reviewer #4 agreed
On 28 Sep, 2020
Reviewer #1 agreed
On 25 Sep, 2020
Reviewer #2 agreed
On 25 Sep, 2020
Reviewers invited
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On 19 Sep, 2020
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On 18 Sep, 2020
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On 18 Sep, 2020
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Editorial decision: Major Revision
On 19 Aug, 2020
Review #6 received
Received 17 Aug, 2020
Review #4 received
Received 15 Aug, 2020
Reviewer #10 agreed
On 13 Aug, 2020
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Received 13 Aug, 2020
Review #5 received
Received 09 Aug, 2020
Reviewer #9 agreed
On 08 Aug, 2020
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On 06 Aug, 2020
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Received 06 Aug, 2020
Review #7 received
Received 06 Aug, 2020
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Received 04 Aug, 2020
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On 04 Aug, 2020
Reviewer #5 agreed
On 04 Aug, 2020
Reviewer #6 agreed
On 04 Aug, 2020
Reviewer #7 agreed
On 04 Aug, 2020
Reviewer #1 agreed
On 03 Aug, 2020
Reviewer #2 agreed
On 03 Aug, 2020
Reviewer #3 agreed
On 03 Aug, 2020
Review #1 received
Received 03 Aug, 2020
Review #2 received
Received 03 Aug, 2020
Reviewers invited
Invitations sent on 02 Aug, 2020
Editor assigned
On 30 Jul, 2020
Submission checks complete
On 29 Jul, 2020
Editor invited
On 29 Jul, 2020
First submitted
On 24 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Information Retrieval and Management
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Big Data.
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 from previous work. 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
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