Ant Colony Optimization-Based Energy Efficiency Optimization in IoT-Enabled Cloud Computing Resource Allocation

Efficient resource allocation methods are crucial in cloud computing environments during the Internet of Things (IoT) era. Significant challenges in processing, analyzing, and storing data arise from the vast amounts of data generated by billions of IoT devices. This paper proposes a new method to improve energy efficiency in cloud computing resource allocation using Ant Colony Optimization (ACO). The ACO algorithm, inspired by the foraging behavior of ants, excels at finding the most efficient routes through graphs and was developed to address complex optimization issues in distributed computer systems. Achieving adequate Quality of Service (QoS), such as improved throughput and minimized latency, is a key goal of our multi-objective optimization approach. Another primary objective is to reduce energy consumption. The proposed architecture automatically allocates resources among cloud servers and IoT devices, identifying the most energy-efficient pathways for data processing and storage by mimicking the behavior of ants. Experiments conducted in a simulated cloud computing environment with IoT applications show that the ACO-based method outperforms conventional approaches in terms of energy efficiency, resource utilization, and operating cost reduction. This research contributes to the sustainable development of cloud computing infrastructures capable of meeting the growing demands of IoT applications, thereby facilitating the emergence of environmentally friendly and cost-efficient computing paradigms.