Abstract: Cloud scheduling strategy based on improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm was proposed to solve the problem of low efficiency of task scheduling in the implementation of cloud computing environment. Firstly, an improved DBSCAN clustering algorithm was used to cluster tasks. Secondly, the classified tasks were matched with classified resources to solve the low matching degree in resources and tasks. Experiments showed that the average execution time of tasks on the terminal was reduced by about 35.2% after clustering task, and the task scheduling time had also been significantly reduced.

Key words: task scheduling, cloud computing environ ment, cluster