Website cloud is a multi-tenant cloud deployment architecture. This thesis studies the resources scheduling policy of the Eucalyptus-based website cloud, and the HTTP request as fine-grained task for the greedy scheduling algorithm. It also discusses the model of weighted machine learning algorithms, that the system can take the initiative to perceive the request peak and load balancing. Experiments show that the ideal.