Our research group is pleased to announce the publication of a new article titled “Optimizing Cloud Service Composition with Cuckoo Optimization Algorithm for Enhanced Resource Allocation and Energy Efficiency” in Future Internet, published by MDPI.

​This research addresses the challenges of cloud service composition—particularly resource allocation, load balancing, task scheduling, and energy management—within dynamic and variable cloud environments. The study introduces an enhanced approach using the Cuckoo Optimization Algorithm (COA), modeling high-demand services as original host eggs and low-demand services as cuckoo eggs competing for resources.

Using CloudSim 5.0, COA was evaluated against Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) across key performance metrics. Results showed that COA significantly outperformed PSO and ACO, achieving superior task scheduling efficiency, dynamic load balancing, energy-aware resource allocation, reduced operational costs, lower SLA violations, and higher task completion and VM utilization rates.

These outcomes highlight COA as a robust, scalable, and highly effective solution for optimizing cloud service composition and improving overall cloud system performance.​

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