Adaptive Energy Optimization in Cloud Computing Through Containerization

Cloud computing has significantly transformed the way organizations utilize computing resources by offering scalability, flexibility, and cost-effectiveness. Energy consumption has become a major research concern with the growing demand for cloud services these days. Adaptive energy optimization techniques are very much essential to minimize energy usage across cloud servers. The main objective of this research is to conserve energy across heterogeneous cloud servers in datacentres. The proposed adaptive energy optimization framework uses container orchestration utilizing Kubernetes framework by sharing the host system kernel and reducing resource overhead to maximize energy efficiency. The work also incorporates smart algorithms, including Ant Colony Optimization (ACO), Swarm Intelligence, and Dynamic Voltage and Frequency Scaling (DVFS) to enable the real-time, energy cognizant distribution of workloads according to the current demand, avoiding unnecessary consumption of resources. Kubernetes container orchestration to optimize energy usage, facilitating effective scaling and thus simplifying the management of applications. Two novel components, the Adaptive Resource Adjustment Algorithm (ARAA) and the Automated Resource-Aware Kubernetes Lifecycle Management (ARKLM), work together to dynamically adjust resource allocation and deactivate idle containers within the Kubernetes environment. Experimental results confirm the framework’s effectiveness, demonstrating that the DVFS algorithm achieved a low 0.94% average CPU usage, while the ACO algorithm delivered the fastest execution time. This dual-focus on energy efficiency and speed makes the framework a scalable and sustainable solution for modern cloud operations, aligning with UN Sustainable Development Goals (7, 9, 12, 13, 17) by enabling energy-efficient, low-carbon, and collaborative cloud operations through intelligent automation.