Energy efficiency optimization in big data processing platform by improving resources utilization

Song, Jie; Ma, Zhongyi; Thomas, Richard; Yu, Ge

doi:10.1016/j.suscom.2018.11.011

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…e statistical cloud platform data traffic is unknown. rough the separate statistics of the subunits, the results are finally summarized [15,16]. erefore, the number of iterations can be estimated according to the coverage length of the corresponding operation data, as shown in the following formula:…”

Section: Introductionmentioning

confidence: 99%

Virtual Simulation Management of Data Traffic Optimization of Big Data Cloud Platform considering Multipoint Mapping Algorithm

2021

Advances in Multimedia

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With the continuous development of Internet, cloud computing, and other technologies, build a cloud platform based on Cloud Computing Center, but how to effectively carry out operation and maintenance and face users to ensure the continuity and effectiveness of the platform is extremely important. In view of these needs and limitations, this paper introduces the multipoint mapping algorithm, combs the statistical methods of platform cloud traffic, carries out platform data traffic by classification, constructs the data traffic optimization management model, analyzes the relevant data samples, carries out statistical calculation for data diversion tasks, analyzes and processes the priority indicators, and forms the final results through continuous iteration, realizing the management of data flow optimization virtual simulation of big data cloud platform. Simulation results show that the multipoint mapping algorithm is effective and can effectively support the data flow of big data cloud platform and optimize virtual simulation management.

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Section: Introductionmentioning

confidence: 99%

Virtual Simulation Management of Data Traffic Optimization of Big Data Cloud Platform considering Multipoint Mapping Algorithm

2021

Advances in Multimedia

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“…A heuristic-centric method changed into proposed in [12] as a option to the Multi be a part of question Ordering (or MJQO) problem. This algorithm blended "2" critical seek algorithms: cuckoo and tabu seek.…”

Section: Literature Surveymentioning

confidence: 99%

An Enhanced Query Optimization Implemented in Hadoop using Bio-Inspired Algorithm with HDFS Technique

Abhijit Banubakode

2023

IJRITCC

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A more effective method for massive data query optimization using HDFS and the Bio-inspired algorithm. Big Data configuration and query optimization are the two phases of the process. To remove redundant data, the input data is first per-processed using HDFS. Then, utilizing entropy calculation, features like closed frequent pattern, support, and confidence are extracted and managed. The Bio-inspired Horse Herd approach is used to group pertinent information based on this outcome. In the second step, the Big Data queries are used to obtain the same features. The optimized query is then located using the Bio-inspired technique, and the similarity assessment procedure is run. The proposed algorithm, according to this research, outperforms other ones that is unique in use. It is challenging to determine the veracity of this claim without more information regarding the experimental setup and the precise measures employed to assess the algorithm's effectiveness. Furthermore, it is unknown how the proposed algorithm stacks up against other cutting-edge query optimization methods. Finally, the assess has efficiency of using this method, more optimistic query achieved and comparison analysis are proved.

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“…C1 market segmentation 1,3,4,5,6,7,8,9,10,11,12,13 From the ISM model structure of the influence factor, we can see that market segmentation (C1), value content(C2) and community building(C4) are the root causes of the impact of big data on the platform business model. Thus, the way in which to effectively control the tracking and control of big data becomes a key focus.…”

Section: L(f I ) P(f I ) C(f I ) = L(f I )\P(f I )mentioning

confidence: 99%

“…With the development, popularization and application of information technology, the use of big data has penetrated the daily lives of ordinary people and created unprecedented opportunities for enterprises to use their data assets to conduct market activities. Google, Amazon, Facebook and other companies have undertaken great efforts in industrial operations by collecting and utilizing big data [1]. Determining how to use big data to achieve their own leapfrog development over competitors has gradually become the main task of platform enterprises.…”

Section: Introductionmentioning

confidence: 99%

Assessing the application of big data technology in platform business model: A hierarchical framework

Du¹,

Gao²,

Chang³

et al. 2020

PLoS ONE

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This research aims to create a hierarchical framework for the development of a platform business model based on big data. However, this hierarchical framework must consider unnecessary attributes and the interrelationships between the aspects and the criteria. Hence, fuzzy set theory is used for screening out the unnecessary attributes, a decisionmaking and trial evaluation laboratory (DEMATEL) is proposed to manage the complex interrelationships among the aspects and attributes, and interpretive structural modeling (ISM) is used to divide the hierarchy and finally construct a hierarchical framework. The results reveal that (1) value proposition and community building in value production are fundamental links; (2) information technology and information management in value production are technical supports; (3) customer development in value marketing is the power source; and (4) value acquisition is the last link, which is established on the basis of and influenced by value marketing and value network. This hierarchical framework aims to guide the platform toward the application of big data. This study also proposes engagement of stakeholders for promoting value creation and establishing a sound business model from multiple levels and links.

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Energy efficiency optimization in big data processing platform by improving resources utilization

Cited by 7 publications

References 27 publications

Virtual Simulation Management of Data Traffic Optimization of Big Data Cloud Platform considering Multipoint Mapping Algorithm

Virtual Simulation Management of Data Traffic Optimization of Big Data Cloud Platform considering Multipoint Mapping Algorithm

An Enhanced Query Optimization Implemented in Hadoop using Bio-Inspired Algorithm with HDFS Technique

Assessing the application of big data technology in platform business model: A hierarchical framework

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