Title: Scalable and adaptable hybrid LSTM model with multi-algorithm optimisation for load balancing and task scheduling in dynamic cloud computing environments

Authors: Mubarak Idris; Mustapha Aminu Bagiwa; Muhammad Abdulkarim; Nurudeen Jibrin; Mardiyya Lawal Bagiwa

Addresses: Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria ' Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria ' Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria ' Centre for Cyberspace Studies, Nasarawa State University, Keffi, Nigeria ' Department of Computer Science and Information Technology, AlQalam University Katsina, Nigeria

Abstract: Cloud computing delivers scalable, flexible resources, but dynamic workloads challenge efficient resource management, especially in load balancing and task scheduling. Addressing these challenges is vital for optimal performance, cost efficiency, and meeting growing application demands. This study proposes the MultiOpt_LSTM model, a hybrid approach that integrates long short-term memory (LSTM) networks with multi-algorithm optimisation techniques, including binary particle swarm optimisation (BPSO), genetic algorithm (GA), and simulated annealing (SA). The goal is to optimise resource allocation, reduce response times, and ensure balanced workload distribution across virtual machines. The proposed model is evaluated using both real-world and simulated cloud environments, comparing its performance with state-of-the-art techniques such as ANN-BPSO and heuristic-FSA. Key performance indicators like response time, resource utilisation, and degree of imbalance are used to measure efficiency. Results show that the MultiOpt_LSTM model outperforms competing methods, achieving near-zero imbalance at higher task volumes and demonstrating superior resource utilisation and reduced response times. For example, at 3,000 tasks, the model maintains a balanced distribution, outperforming traditional methods like IBPSO-LBS by a significant margin. While the simulation results are promising, future work will focus on real-world implementations to assess the model's scalability and adaptability in diverse cloud environments.

Keywords: cloud computing; load balancing; task scheduling; hybrid LSTM model; optimisation algorithms; resource utilisation; response time; degree of imbalance.

DOI: 10.1504/IJCC.2025.148719

International Journal of Cloud Computing, 2025 Vol.14 No.3, pp.241 - 261

Received: 26 Oct 2024
Accepted: 08 Apr 2025
Published online: 21 Sep 2025 *

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