Focusing on the issue that the ensemble classifier based on weak classifiers needs to sacrifice a lot of training time to obtain high precision, an ensemble method of strong classifiers based on instances named Fast Strong-classifiers Ensemble (FSE) was proposed. Firstly, the evaluation method was used to eliminate substandard classifier and order the restclassifiers by the accuracy and diversity to obtain a set of classifiers with highest precision and maximal difference. Secondly, the FSE algorithm was used to break the existing sample distribution, to re-sample and make the classifier pay more attention to learn the difficult samples. Finally, the ensemble classifier was completed by determining the weight of each classifier simultaneously. The experiments were conducted on UCI dataset and customized dataset. The accuracy of the Boosting reached 90.2% and 90.4% on both datasets respectively, and the accuracy of the FSE reached 95.6% and 93.9%. The training time of ensemble classifier with FSE was shortened by 75% and 80% compared to the ensemble classifier with Boosting when they reached the same accuracy. The theoretical analysis and simulation results show that FSE ensemble model can effectively improve the recognition accuracy and shorten training time.

For the problem that task scheduling program in cloud computing environments usually takes high response time and communication costs, a Honey Bee Behavior inspired Load Balancing (HBB-LB) algorithm was proposed. Firstly, the load was balanced across Virtual Machines (VMs) for maximizing the throughput. Then the priorities of tasks on the machines were balanced. Finally, HBB-LB algorithm was used to improve the overall throughput of processing, and priority based balancing focused on reducing the wait time of tasks on a queue of the VM. The experiments were carried out in cloud computing environments simulated by CloudSim. The experiment results showed that HBB-LB algorithm respectively reduced average response time by 5%, 13%, 17%, 67% and 37% compared with Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Dynamic Load Balancing (DLB), First In First Out (FIFO) and Weighted Round Robin (WRR) algorithms, and reduced maximum completion time by 20%, 23%, 18%, 55% and 46%. The result indicates that HBB-LB algorithm is suitable for cloud computing system and helpful to balancing non-preemptive independent tasks.