@article {10.3844/jcssp.2026.2092.2103,
article_type = {journal},
title = {Masterpiece Optimization Algorithm-Based Priority-Aware Load Balancing Strategy for Cloud Data Centers},
author = {Vijaykumar, S and Chandre, Shanker},
volume = {22},
number = {7},
year = {2026},
month = {Jul},
pages = {2092-2103},
doi = {10.3844/jcssp.2026.2092.2103},
url = {https://thescipub.com/abstract/jcssp.2026.2092.2103},
abstract = {Cloud Computing (CC) is one of the widely used technologies due to its advanced features such as pay-per-use, scalability, and flexibility. The primary objective of CC is to allow users to access and purchase cloud services that are on demand through internet-based applications. Efficient load-balancing in the cloud faces challenges of high-dimensional state spaces and scalability with increasing tasks. To solve this problem, the Masterpiece Optimization Algorithm (MOA) with a priority constraint is employed for load-balancing according to the tasks efficiently. The MOA is integrated with a priority-based cost function to enhance the task scheduling process by introducing a multi-dimensional approach for load balancing. The priority-based framework helps the scheduler to dynamically recalibrate workloads. The experimental results achieve a total energy consumption of 39.8 W and an average CPU resource utilization of 99.54%, which is better than the existing algorithms, such as the hybrid Particle Swarm Grey Wolf Optimization (PSGWO) algorithm.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}