SAIR: significance-aware approach to improve QoR of big data processing in case of budget constraint

Ahmadvand, Hossein; Goudarzi, Maziar

doi:10.1007/s11227-019-02797-7

Cited by 15 publications

(19 citation statements)

References 31 publications

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“…We can process each data part with different infrastructure or capacity. Furthermore, as we have 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 the reduction of power/energy consumption in the big data processing.…”

Section: Introductionmentioning

confidence: 84%

“…Line 5 uses sampling to discover the variety in initial blocks. By using this sampling method, we are able to estimate the number of required resources for processing for each block [2], [9]. Line 6 estimates the suitable frequency for processing of B i based on the time slot and the amount of processing needed for the mentioned data block.…”

Section: Methodsmentioning

confidence: 99%

“…We have presented the definition of accumulative application in [22]. These type of applications is an important type of Big Data applications [2], [22].…”

Section: Fig 12 Sensitivity Analysis To Deadlinementioning

confidence: 99%

“…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. Besides based on the [2] , [3] and [4], we have shown that by the MapReduce processing, we able to manage the input data and divide it into some parts. We can process each data part with different infrastructure or capacity.…”

Section: Introductionmentioning

confidence: 99%

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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 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.

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Section: Introductionmentioning

confidence: 84%

Section: Methodsmentioning

confidence: 99%

“…We have presented the definition of accumulative application in [22]. These type of applications is an important type of Big Data applications [2], [22].…”

Section: Fig 12 Sensitivity Analysis To Deadlinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…They have not considered the data variety/ skew in their study. We have also considered data variety and reduced the processing resources such as energy or cost [21], [22].…”

Section: Fig 4 the Categories Of 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 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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A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Mostafa

2023

IEEE Access

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Cloud computing technology has undergone tremendous growth in recent years, and there are now many cloud service providers (CSPs). This makes CSP selection a challenging process for cloud users. Further complications arise when users modify the priorities of their requirements. Moreover, concerns such as complex computation, inconsistencies, and rank reversal have been raised in current approaches, resulting in less reliable results. This study presents a new hybrid multiple-criteria decision-making (MCDM) framework for ranking cloud services based on Markov chains combined with the best-only method (BOM). The Markov chain is used to record and track the changes in the priorities of user requirements and determine their final values. Then, the BOM method is utilized to determine the final weights of the QoS criteria based on pairwise comparisons made by the cloud user or decision maker and the final priorities of the user's requirements. Finally, the cloud services are ranked, and the best CSP is selected. The proposed framework was validated using a case study and a real dataset. Performance, consistency, rank conformance, and sensitivity analyses were performed to evaluate the proposed framework. The obtained results prove that the proposed framework is computationally efficient and fully consistent, considers the user requirements and its transition pattern, and is robust to rank reversal.. INDEX TERMS Cloud computing (CC), cloud service selection, QoS criteria, cloud service providers (CSPs), multiple-criteria decision-making (MCDM), best-only method (BOM), Markov chains, Rank reversal.

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SAIR: significance-aware approach to improve QoR of big data processing in case of budget constraint

Cited by 15 publications

References 31 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

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

A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

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