Research Article
Efficient load balancing Adaptive BNBKnapsack Algorithm for Edge computing to improve performance of network
@ARTICLE{10.4108/eetsis.3924, author={Malti Nagle and Prakash Kumar}, title={Efficient load balancing Adaptive BNBKnapsack Algorithm for Edge computing to improve performance of network}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={11}, number={3}, publisher={EAI}, journal_a={SIS}, year={2023}, month={9}, keywords={IoT, Cloud Computing, Context aware system, Scheduling, load balancing algorithm, EEG sensor, BLE, CloudSim, iFogSim, Stress related health issues, Adaptive BNBKnapsackAlgorithm Introduction}, doi={10.4108/eetsis.3924} }- Malti Nagle
Prakash Kumar
Year: 2023
Efficient load balancing Adaptive BNBKnapsack Algorithm for Edge computing to improve performance of network
SIS
EAI
DOI: 10.4108/eetsis.3924
Abstract
INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors. OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth. METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same. RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm.
Copyright © 2023 M. Nagle et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
