Intelligent Workflow Scheduling for Distributed Data Processing Systems

Abstract

The rapid growth of data-intensive applications in scientific computing, enterprise analytics, and cloud services has increased the demand for efficient distributed data processing systems. Traditional scheduling methods like FCFS, Round Robin, and heuristic approaches often fail to meet the dynamic and heterogeneous requirements of modern environments. This paper proposes an intelligent workflow scheduling framework that improves performance through adaptive decision-making, predictive analytics, and machine learning. The system dynamically allocates tasks based on resource availability, workflow dependencies, and historical execution data, enabling it to anticipate bottlenecks and reassign tasks proactively. It also incorporates resource heterogeneity modeling and dependency-aware scheduling to reduce idle time and optimize execution. Performance is evaluated using metrics such as makespan, throughput, resource utilization, and fault tolerance, showing significant improvements over traditional methods. The framework also addresses key challenges like load balancing, scalability, energy efficiency, and fault tolerance. Overall, the proposed approach enhances system efficiency and scalability while supporting integration with emerging technologies such as edge computing and hybrid cloud environments, paving the way for more autonomous and resilient distributed scheduling systems.