Considering the problems of low load rate of data centers in cloud providers, uncertainty and variety of cloud user demand; in order to improve the average profit of the cloud providers, an overbooking model of multiple instances under uncertain demand was established. The proposed model combined the influences of overbooking for cloud data center load balancing and Service Level Agreement (SLA) under the actual cloud computing resource market, multi-constraint of overbooking was provided, then the optimal allocation policy of each instance type was put forward. The simulation results show that when the unused rate of reservation is 0.25, the average profit is relatively high, the load rate of data center is 78%, finally the optimal allocation of each instance type is determined.

Current mobile recommendation systems limit the real role of location information, because the systems just take location as a general property. More importantly, the correlation of location and the boundary of activities of users have been ignored. According to this issue, personalized recommendation technique for mobile life services based on location cluster was proposed, which considered both user preference in its location cluster and the related weight by forgetting factor and information entropy. It used fuzzy cluster to get the location cluster, then used forgetting factor to adjust the preference of the service resources in the location cluster. Then the related weight was obtained by using probability distribution and information entropy. The top-N recommendation set was got by matching the user preference and service resources. As a result, the algorithm can provide service resources with high similarities with user preference. This conclusion has been verified by case study.