Optimal Workflow Scheduling in Cloud Computing Based on Hybrid Bacterial Evolutionary and Bees Mating Optimization Algorithm

Abstract

Cloud computing has emerged as a rapidly maturing paradigm that delivers software applications and hardware infrastructure as services through Service Level Agreements (SLAs). A critical challenge in this environment is the efficient scheduling of interdependent tasks across virtual machines (VMs) while minimizing resource consumption and meeting Quality of Service (QoS) requirements. This paper proposes a Hybrid Optimization Workflow Scheduling (HOWS) algorithm that integrates Bees Mating Optimization (BMO) for global resource exploration and Bacterial Evolutionary Algorithm (BEA) for adaptive local refinement. The hybrid framework improves workflow scheduling by ensuring balanced task allocation, optimal VM utilization, enhanced energy efficiency, and system scalability. Simulation experiments conducted in CloudSim demonstrate that the proposed model significantly outperforms traditional scheduling algorithms such as Round Robin and Shortest Job Next in terms of makespan reduction, workload balancing, and overall throughput. The results establish HOWS as an effective and secure scheduling strategy, contributing to improved resource management and robust performance in dynamic cloud environments. Future directions include