Energy-aware Priority-Based Task Scheduling in Cloud Data Centers Using Bacterial Foraging Optimization

Cloud computing is growing exponentially, and data centers consume more and more energy. As a result, developing energy-efficient task scheduling algorithms has emerged as a prominent research problem and challenge. This paper presents a new method for priority-aware task scheduling in cloud data centers using the Hyper-Heuristic Bacterial Foraging Optimization (BFO-HH) algorithm. In addition, it introduces a new approach to dynamically selecting and combining 4 low-level heuristics (Task Selection, Virtual Machine Migration, Load Balancing, Resource Consolidation) to minimize operational cost, reduce energy consumption, and improve Quality of Service (QoS). All experiments were performed using the CloudSim 3.0.3 toolkit, over heterogeneous synthetic workloads comprising between 20 and 200 cloudlets. The performance of BFO-HH is compared with four well-known metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC). The experimental results demonstrate that, under the tested CloudSim environment and for workloads ranging from 20 to 200 tasks, BFO-HH consistently outperforms all comparative algorithms across multiple metrics. For example, for 200 tasks, BFO-HH exhibits 9.9% less energy consumption, 9.3% achieves shorter makespan, reduces Service Level Agreement (SLA) violations by 28%, increases resource utilization by 7%, and reduces operational cost by 14%. against the state-of-the-art base algorithms. Albeit such improvements are statistically significant, as evidenced by standard deviations and a 95% confidence interval.