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 Qian is with College of Computer and Communication Engineering, University of Petroleum, China (e-mail: email@example.com).
Cite: Qian Zhang, Hong Liang, and Yongshan Xing, "A Parallel Task scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing Environment," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 437-444, 2014.