Energy-Efficient Data Offloading using Data Access Strategy-Based Data Grouping Scheme
| International Journal of Electronics and Communication Engineering |
| © 2023 by SSRG - IJECE Journal |
| Volume 10 Issue 5 |
| Year of Publication : 2023 |
| Authors : Prabhu Shankar, Sharon M, Viji, Rajkumar, Vetrimani, R. Surendiran |
10.14445/23488549/IJECE-V10I5P103
How to Cite?
Prabhu Shankar, Sharon M, Viji, Rajkumar, Vetrimani, R. Surendiran, "Energy-Efficient Data Offloading using Data Access Strategy-Based Data Grouping Scheme," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 5, pp. 28-37, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I5P103
Abstract:
With technological advancements, the large volume of data generated every day is increasing exponentially. The primary data source is smart devices, the Internet of Things (IoT), and social communication network groups. This paved the way for cloud computing to offer massive storage capacity and powerful computational resources. The conventional method of processing data in the cloud is not scalable and cannot meet the requirements of latency-critical applications. The main problem arises when a single data processing task requires multiple data items stored in different data servers. Efficient data placement minimizes the data access cost and data scheduling between the globally distributed data centres. Giving high priority to this alarming issue, this work aimed to propose an efficient data placement method called Data Access Strategy based Data Grouping scheme (DAS-DGS). The existing data sets are distributed to the data centres using access similarity measures, and the newly generated datasets are also dynamically placed on the appropriate servers in the cloud. Experimental simulations show that the proposed method meets multiple objectives: effective energy-efficient data placement scheme for IoT data, reduces the data access cost, minimization access time, average resource utilization, and energy consumption rate. The proposed DAS-DGS shows a promising result compared to the existing methods.
Keywords:
IoT, Cloud computing, Data placement, Latency, Access cost, Machine learning.
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