Abstract-Parallel task scheduling is one of the core problems in the field of cloud computing research area, which mainly researches parallel scheduling problems in cloud computing environment by referring to the high performance computing required by massive oil seismic exploration data processing. Because of the natural reparability of Seismic data, it should maximize the use of computing resources to put the job file to the resource nodes, which can just meet the task computing requirements. This paper proposes scheduling optimization strategy of task and resource hybrid clustering based on fuzzy clustering, conducts the the clustering partition solution of concurrent job according to matching degree of task and resource nodes and narrows task scheduling scale and, narrows task scheduling scale and at the same time lays the foundation for dynamic acheduling tasks. After the division is completed, improved Bayesian classification algorithm is introduced to fast match tasks and computer according to realtime load and queue operations. In the end, verified by experiments, this scheme has higher efficiency. Index Terms-Cloud computing, parallel scheduling, fuzzy clustering, task and resource hybrid clustering, Bayesian classification algorithm.
Zhang et al. REF proposed a scheduling optimization strategy of task and resource hybrid clustering based on fuzzy clustering to narrow the task scheduling scale and introduced an improved Bayesian classification algorithm that fast matches tasks and computers.
A Parallel Task scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing Environment
International Journal of Machine Learning and Computing
Computer Science