Using Data Variety for Efficient Progressive Big Data Processing in Warehouse-Scale Computers

Ahmadvand, Hossein; Goudarzi, Maziar

doi:10.1109/lca.2016.2636293

Cited by 19 publications

(18 citation statements)

References 9 publications

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“…Due to the structure of MapReduce processing and 4'v of Big Data, Big Data processing is a suitable area to apply the power reduction techniques such as DVFS. Furthermore, as we shown in the previous work [2] data variety that is one of the important features of big data causes variation in resource consumption. This fact makes DVFS a suitable technique for reduction of power/energy consumption in big data processing.…”

Section: Introductionmentioning

confidence: 79%

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DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

Ahmadvand

Foroutan

2020

Preprint

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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 consumption of processing resources such as CPU consumption. In this paper, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider a deadline as our constraint and before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. We have used a set of data sets and applications in the evaluation phase. 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.

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Section: Introductionmentioning

confidence: 79%

“…We have presented the definition of accumulative application in [22]. This type of applications is an important type of Big Data applications [2], [22]. 6 CONCLUSION In summary, we have studied the impact of data variety on CPU utilization and energy consumption for Big Data processing.…”

Section: Fig 13 Sensitivity Analysis To Deadlinementioning

confidence: 99%

DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

Ahmadvand

Foroutan

2020

Preprint

Self Cite

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“…In turn, the Root Mean Squared Error (RMSE) measures the average magnitude of the error, which is the square root of the average of squared differences between prediction and actual observation. The equation is (5).…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…Another option resides on progressive computation i.e. when large data space is pruned progressively to look for the results [5]. The processing times were affordable, but this method could be used if scholars decide to extend the time period of data collection or the number of hashtags and get a larger dataset.…”

Section: Computational Timementioning

confidence: 99%

Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections

2020

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For more than a decade now, with the emergence of Internet 2.0, users have been able to generate their own content and share it publicly with relative ease. This boom has witnessed a huge surge in the popularity of Online Social Networks (OSN), in particular the Twitter microblogging platform which allows its users to share text messages of up to 140 characters with their family, friends and followers [12]. More than 500 million messages, commonly known as tweets, are posted every day [20, 34]. Given that the main reason behind these publications is to express the opinion of the users, they have proven to be great interest to be analysed [14, 38, 48]. This huge amount of content circulating in OSN has attracted the attention of marketing agencies that seek capitalize on the behaviour of clients (current or future) to adjust their online campaigns and even use the social content (number of likes, retweets, etc.) to predict consumer behaviour in the real world [45, 53]. Recently this type of analysis

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“…They have compared their work with the default Spark Scheduler. VM migration and scale down in case of low performance are considered in [8]. The authors in [9] have considered the variation of application requirements in Big Data in case of choosing cloud as an infrastructure for processing.…”

Section: Related Workmentioning

confidence: 99%

DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

Ahmadvand

Foroutan

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Using Data Variety for Efficient Progressive Big Data Processing in Warehouse-Scale Computers

Cited by 19 publications

References 9 publications

DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections

DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

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