Cloud Parallel Task Scheduling Algorithm Based on Fuzzy Clustering

Parallel task scheduling is one of the key problems in the field of cloud computing research area,which mainly researches parallel scheduling problems in cloud computing environment by the reference to the high performance computing required by massive oil seismic exploration data processing.Because of the natural reparability of Seismic data,it can maximize the full use of computing resources to put the job file to the resource nodes,which can just meet the task computing requirements.This paper proposed scheduling optimization strategy of task and resource hybrid clustering based on fuzzy clustering.The strategy takes matching degree of task and resource nodes as reference and with the clustering partition solution of concurrent job,narrows task scheduling scale and at the same time,lays foundation for the dynamic scheduling of tasks.After the division is completed,improved Bayes classification algorithm is introduced to fast match tasks and computer according to real-time load and queue operations.In the end,the experiments verify that this scheme has higher efficiency.