A Multi-Objective Data Placement Method for IoT Applications Over Big Data Using NSGA-II

Xu, Xiaolong; Gu, Renhao; Yuan, Yuan; Peng, Kai; Chen, Yadang; Dou, Wanchun; Liu, Alex X.

doi:10.1109/cybermatics_2018.2018.00108

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…However, the data access time of each application depends not only on the placement of historical data but also on the placement of each workflow and input data. Based on the overall placement of meteorological historical data, we continue to study how to collaboratively place the input data and tasks of workflows on the corresponding nodes in cloud centre [8, 10], thereby further reducing the average data access time for all tasks in workflows [11, 12]. As the number of meteorological workflows and data offloaded to cloud centre increases rapidly [13, 14], increasing the resource utilisation of active nodes in cloud centre is also being paid more and more attention [15, 16], and it has become an important indicator to measure the performance of placement method [17, 18].…”

Section: Introductionmentioning

confidence: 99%

Data privacy‐based coordinated placement method of workflows and data

Huang

Xue

Hu³

et al. 2020

IET cyber-phys. syst.

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With the rapid development of data acquisition technology, many industries data already have the characteristics of big data and cloud technology has provided strong support for the storage and complex calculations of these massive data. The meteorological department established the cloud data centre based on the existing storage and computing resources and rearranged the historical data to reduce the historical data access time of applications. However, the placement of each workflow and input data also affects the average data access time, which in turn affects the computing efficiency of the cloud data centre. At the same time, because of the collaborative processing of multiple nodes, the resource utilisation of cloud data centre has also been paid more and more attention. In addition, with the increase of data security requirements, some privacy conflict data should avoid being placed on the same or neighbouring nodes. In response to this challenge, based on the fat-tree network topology, this study proposes a data privacy protection-based collaborative placement strategy of workflow and data to jointly optimise the average data access time, the average resource utilisation, and the data conflict degree. Finally, a large number of experimental evaluations and comparative analyses verify the efficiency of the proposed method. T m Total data access time of the task t m T avg AC average data access time for M tasks U i col resource utilisation of the ith compute node U j sto resource utilisation of the jth storage node U average resource utilisation of all nodes C data conflict degree for all conflicting data inp

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