Title: BERA-CLOUD: resource allocation in cloud computing using a bald eagle optimised spiking neural network

Authors: Nikhil Kumar Marriwala; Sunita Panda; Priya Dasarwar; Pooja Singh; C. Gnana Kousalya

Addresses: Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India ' Department of EECE, MURTI Research Centre, NEXTGEN Connectivity Lab, GITAM (Deemed to be University), Bengaluru Campus, Karnataka, India ' Department of CSE, Symbiosis Institute of Technology, Symbiosis International (Deemed to be University), Nagpur Campus, Pune, India ' Department of SCSE/CSE, Galgotias University, Greater Noida, Uttar Pradesh, India ' Department of Electronics and Communication Engineering, St. Joseph's Institute of Technology, Chennai – 119, India

Abstract: In this paper, a novel bald eagle optimised resource allocation in cloud is developed for effective resource management and dynamic task scheduling in cloud networks. Initially, the user provides a task list that is needed to be scheduled and prioritised. The task list is fed into a spiking neural network to identify patterns and prioritise tasks based on their importance. The prioritised tasks are then used for dynamic prediction, which involves forecasting future states or requirements. It also makes it easy for the system to adapt to change since is able to estimate other future resource necessities or modification in tasks. The anticipated tasks are then solved by applying the bald eagle search algorithm, which is designed to find effective solutions aligning between exploitation and exploration to reach the best solutions within the identified search space functions. The optimisation process interacts with cloud storage to retrieve and store data.

Keywords: internet of things; IoT; cloud networks; bald eagle search algorithm; spiking neural network; SNN; virtual machines; cloud computing.

DOI: 10.1504/IJCC.2025.148724

International Journal of Cloud Computing, 2025 Vol.14 No.3, pp.315 - 326

Received: 01 Mar 2025
Accepted: 16 Apr 2025
Published online: 21 Sep 2025 *

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