云资源的动态变化和不确定性给资源管理及任务调度带来了很大的困难.为了准确地掌握资源动态负载和可用能力信息，提出一种基于熵优化和动态加权的资源评估模型，其中，熵优化模型利用最大熵和熵增原理的目标函数及约束条件，筛选出满足用户 QoS 和系统最大化的资源，实现最优调度，保障用户 QoS.对筛选后的资源再进行动态加权负载评估，对负载过重及长期不可用资源进行迁移、释放等，可减少能耗，实现负载均衡和提高系统利用率.设计了仿真实验，以验证所提评估模型的性能.实验结果表明，熵优化模型对用户 QoS 和系统最大化有很好的效果，动态加权负载评估有利于均衡负载，提高系统利用率.该评估模型实现了用户QoS保障、减少能耗、负载均衡以及提高系统利用率等多目标的优化.%The dynamic and uncertainty of cloud resource makes resource allocation and task scheduling more difficult. In order to retrieve accurate resource information about dynamic loads and available capacity, this study proposes a resource evaluation model based on entropy optimization and dynamic weighting. The entropy optimization filters the resources that satisfy user QoS and system maximization by goal function and constraints of maximum entropy and the entropy increase principle, which achieves optimal scheduling and satisfied user QoS. Then the evaluation model evaluates the load of having filtered resources by dynamic weighted algorithm. In order to reduce energy consumption, achieve load balancing and improve system utilization, the study allows the migration or release the resources which overload and unavailable for a long time. Experimental results show the effect of entropy optimization on user QoS and system maximization, and dynamic weighted algorithm benefits load balancing and system utilization. The experimental results prove that the evaluation model achieves multi-objective optimization such as satisfying user QOS, reducing energy assumption, balancing load, improving system utilization and so on.
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