TY  - JOUR
AU  - Vijaykumar, S 
AU  - Chandre, Shanker 
PY  - 2026
TI  - Masterpiece Optimization Algorithm-Based Priority-Aware Load Balancing Strategy for Cloud Data Centers
JF  - Journal of Computer Science
VL  - 22
IS  - 7
DO  - 10.3844/jcssp.2026.2092.2103
UR  - https://thescipub.com/abstract/jcssp.2026.2092.2103
AB  - 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.