Application scheduling deployment method based on big data cluster and storage medium

The invention discloses an application scheduling deployment method based on a big data cluster and a storage medium. Double-track system parallel synchronous implementation is adopted, and the queuing theory, the cellular automaton and the DS theory are combined, so that task scheduling and resource allocation are optimized, and the system performance and the resource utilization rate are improved. By comprehensively considering a plurality of indexes and utilizing an information fusion technology, the method can make a reliable and efficient task scheduling decision in a dynamic environment; firstly, a task scheduling strategy is optimized on the whole and is not limited to a certain specific performance index; in addition, the cellular automaton model is used for simulating the dynamic change of task scheduling, and the actual execution condition of tasks and the dynamic change of cluster resources can be comprehensively considered. And 2, multi-view analysis is provided: information of two views is fused