ABC: Optimizing Energy and VM Failure Rate for Task Scheduling in Cloud Computing Environment
Rameshwaraiah Kurupati, Santhosh Kumar Medishetti, Golamari Sravan Kumar, Masuram Harshitha, Peddinti Maneesha, Yellu Navitha · 2025
Efficient task scheduling in cloud computing plays a crucial role in enhancing system performance and ensuring optimal resource utilization. This paper presents a task scheduling approach based on the Artificial Bee Colony (ABC) algorithm to address critical challenges such as high makespan, excessive energy consumption, and frequent VM failures. The proposed ABC-based scheduler simulates the intelligent foraging behavior of honeybees to explore and exploit optimal task-to-VM mappings dynamically. The CEA-Curie workload is utilized for realistic and high-performance simulation, and the evaluation is conducted using the CloudSim simulator. Experimental results demonstrate that the proposed model significantly improves scheduling efficiency by reducing the makespan, lowering energy consumption, and minimizing VM failure rates. Specifically, the model achieves improvements of 18.18% in makespan, 16.41% in energy consumption, and 20.16% in VM failure rate compared to traditional scheduling techniques. These results confirm the effectiveness of the ABC algorithm in handling complex scheduling scenarios and enhancing the reliability and sustainability of cloud computing environments.