Workflow Scheduling Scheme for Optimized Reliability and End-to-End Delay Control in Cloud Computing Using AI-Based Modeling

In the context of cloud systems, the effectiveness of placing modules for optimal reliability and end-to-end delay (EED) is directly linked to the success of scheduling distributed scientific workflows. However, the measures used to evaluate these aspects (reliability and EED) are in conflict with each other, making it impossible to optimize both simultaneously. Thus, we introduce a scheduling algorithm for distributed scientific workflows that focuses on enhancing reliability while maintaining specific EED limits. This is particularly important given the inevitable failures of processing servers and communication links. To achieve our objective, we first develop an artificial intelligence-based model that merges an improved version of the wild horse optimization technique with a levy flight approach. This hybrid approach enhances the ability to explore new possibilities effectively. Additionally, we establish a viable strategy for sharing mapping decisions and stored information among processing servers, promoting scalability and robustness—essential qualities for large-scale distributed systems. This strategy not only boosts local search capabilities but also prevents premature convergence of the algorithm. The primary goal of this study is to pinpoint resource placements that strike a balance between global exploration and local exploitation. This entails effectively harnessing the search space and minimizing the inclination toward resources with a high likelihood of failures. Through experimentation in various system configurations, our proposed method consistently outperformed competing workflow scheduling algorithms. It achieved notably higher levels of reliability while adhering to the same EED constraints.