Framework for Optimizing Cloud Workflow Scheduling with the Krill Herd Algorithm

Cloud computing has revolutionized the way compu-tational resources are provisioned and managed, offering scalability, flexibility, and cost-efficiency. Effective workflow scheduling in dynamic cloud environments is paramount to harness these benefits. In this paper, we introduce a novel workflow scheduling framework centered on the Krill Herd Algorithm (KHA). Our approach aims to optimize task allocation and resource utilization while meeting service-level agreements (SLAs) in diverse cloud computing scenarios. The paper begins with a detailed system model, providing insights into the dynamic nature of cloud environments and the associated scheduling challenges. We present the methodology, highlighting the adaptability and efficiency of KHA in addressing real-time changes and resource allocation dynamics. To evaluate our framework, comprehensive experiments are conducted, leveraging key performance metrics, including Task Completion Time (TCT), Resource Utilization (RU), Makespan (MS), and SLA Compliance. Results reveal the superiority of our approach over existing methods, with consistent improvements in task completion efficiency, resource optimization, reduced turnaround times, and SLA adherence.