Advancement in data locality during data retrieval using community mapping of fog nodes

Ghongadi, Sonia Suhas (2021) Advancement in data locality during data retrieval using community mapping of fog nodes. Masters thesis, Dublin, National College of Ireland.

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Abstract

Because of the exponential expansion of IoT devices, enormous amounts of data are generated and collected. Increased inefficiency and high latency in time-sensitive applications have been seen in the Cloud Data Center-to-IoT method of calculation and storage. To overcome these shortcomings new complementary approach to cloud computing has been introduced named Fog Computing. This decentralized approach is gaining attention in building smart cities and healthcare infrastructure. Data management is one of the key components of fog computing, and control of the data locality is also necessary. When data is created and transmitted to the fog layer, the typical technique saves the data in a hazardous way in the network. Due to this reason, in time-sensitive applications, data retrieval requests or decision-making processes take high response time and latency. To address this issue, this research study has implemented an algorithm of community mapping of fog nodes where the data is stored in a covered area or community of fog nodes as well as replicated within the same community. The experimental results have shown that with community mapping, the time taken to execute the application is reduced by approximately 79% as compared to the traditional approach.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Clara Chan
Date Deposited:	13 Oct 2021 17:00
Last Modified:	13 Oct 2021 17:00
URI:	https://norma.ncirl.ie/id/eprint/5085

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