Abstract: With parallel computing, distributed computing and grid computing technology, cloud computing was proposed as a new model and developing fast. Hadoop is an open source cloud computing system that has been widely used.Job scheduling is one of the core problem on Hadoop platform. Through understanding and analyzing current scheduling algorithm that has already existed for Hadoop,based on learning approach, the past history of nodes and job attri-butes were used to improve job scheduling. Feature weighting-based naive bayes classification algorithm was applied to im- prove tasks scheduling, then it was verified through experiments. As a result, it improves the efficiency of scheduling of task allocation for Hadoop.

Key words: Cloud computing,Job scheduling,Feature weighted naive bayes