Open Access

ARTICLE

Energy Efficient and Resource Allocation in Cloud Computing Using QT-DNN and Binary Bird Swarm Optimization

Puneet Sharma¹, Dhirendra Prasad Yadav¹, Bhisham Sharma^2,*, Surbhi B. Khan^3,4,*, Ahlam Almusharraf ⁵

1 Department of Computer Engineering & Applications, G.L.A. University, Mathura, 281406, India
2 Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, India
3 School of Science, Engineering and Environment, University of Salford, Manchester, M5 4WT, UK
4 Division of Research and Development, Lovely Professional University, Phagwara, 144411, India
5 Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

* Corresponding Authors: Bhisham Sharma. Email: ; Surbhi B. Khan. Email:

(This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)

Computers, Materials & Continua 2025, 85(1), 2179-2193. https://doi.org/10.32604/cmc.2025.063190

Received 08 January 2025; Accepted 13 May 2025; Issue published 29 August 2025

Abstract

The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems. This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network (QT-DNN) with Binary Bird Swarm Optimization (BBSO) to enhance resource allocation while preserving Quality of Service (QoS). In contrast to conventional approaches, the QT-DNN accurately predicts task-resource mappings using tensor-based task representation, significantly minimizing computing overhead. The BBSO allocates resources dynamically, optimizing energy efficiency and task distribution. Experimental results from extensive simulations indicate the efficacy of the suggested strategy; the proposed approach demonstrates the highest level of accuracy, reaching 98.1%. This surpasses the GA-SVM model, which achieves an accuracy of 96.3%, and the ART model, which achieves an accuracy of 95.4%. The proposed method performs better in terms of response time with 1.598 as compared to existing methods Energy-Focused Dynamic Task Scheduling (EFDTS) and Federated Energy-efficient Scheduler for Task Allocation in Large-scale environments (FESTAL) with 2.31 and 2.04, moreover, the proposed method performs better in terms of makespan with 12 as compared to Round Robin (RR) and Recurrent Attention-based Summarization Algorithm (RASA) with 20 and 14. The hybrid method establishes a new standard for sustainable and efficient administration of cloud computing resources by explicitly addressing scalability and real-time performance.

---

Keywords

---