Title: AGOA: Adam gazelle optimisation algorithm for collaborative e-learning application in cloud computing with load balancing

Authors: N. Venkatesh Naik; K. Madhavi

Addresses: Department of CSE, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India ' Department of Computer Science and Engineering, JNTUA College of Engineering Anantapur, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India

Abstract: With the dynamic scalability and utilisation of virtualised resources, Cloud Computing (CC) offers a good impact on the educational environment. However, the energy and traffic handling are difficult in the conventional schemes. In this research, a novel strategy named Adam Gazelle Optimisation Algorithm (AGOA) is designed for collaborative E-learning applications with course recommendations in the cloud. Here, the structure of the cloud is analysed and then, the course recommendation approach is designed. According to user access, a Virtual Machine (VM) migration algorithm is established with effectual load balancing in the public subnet of the cloud, wherein the following steps are utilised. Here, tasks are allocated to the VM in a round-robin manner and predict the load of PM, which is carried out by Deep Recurrent Neural Network (DRNN). When the predicted load is greater than the threshold, VM migration is conducted. The objectives considered for this migration are CPU utilisation, energy consumption, makespan and migration cost using the proposed AGOA. The AGOA is the integration of the Adam and Gazelle Optimisation Algorithm (GOA). The performance measure employed for AGOA attained predicted load, energy consumption and migration cost values of 0.139, 0.158 J and 10.1.

Keywords: collaborative e-learning; cloud computing; load balancing; Adam optimisation; Adam; GOA; gazelle optimisation algorithm.

DOI: 10.1504/IJWMC.2025.143036

International Journal of Wireless and Mobile Computing, 2025 Vol.28 No.1, pp.20 - 33

Received: 12 Sep 2023
Accepted: 01 Apr 2024
Published online: 02 Dec 2024 *

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