It is difficult for users to determine the type of cloud hosts on cloud platforms when they are actually using cloud platforms, which results in low utilization of cloud platform resources. In some typical methods to solve the low resource utilization, the placement algorithms are optimized from the perspective of cloud provider, and the user selection will limit the utilization of resources; while in other methods, the collection and prediction of task performance under the cloud platform in a short time are made, but it will reduce the accuracy of task classification. In order to achieve the goals of improving cloud platform resource utilization and simplifying user operations, a multi-attribute task performance collection tool, named Lbenchmark, was proposed to collect the performance characteristics of task comprehensively, and the load was reduced by more than 50% compared with Ganglia. Then, with the performance data, a K-Nearest Neighbor ( KNN) application performance classification algorithm with the multiple K-Dimension ( KD) tree based on configurable weights was proposed. The suitable parameters were selected to establish multiple KNN classifiers with KD tree, and the cross validation method was used to adjust the weight of each attribute in different classifiers. The experimental results show that, compared with the traditional KNN algorithm, the calculation of the proposed algorithm is significantly increased by about 10 times, and its accuracy is averagely improved by about 10%. The proposed algorithm can use data feature mapping to provide resource recommendations to users and cloud providers, improving the overall utilization of cloud platforms.

Concerning the problem that the lack of testing methods would lead to mismatch between supply and demand of private clouds, an adaptive and scalable private cloud system testing method was proposed, which can test private cloud computing ability in IaaS (Infrastructure as a Service). The number of virtual machines was dynamically increased through the private cloud application program interface, hardware information and operating system category of the virtual machine configuration were selected by performance-characteristic model, and different load models were used according to different needs of users to form simulation environment. At last, cloud computing Service Level Agreement (SLA) was used as a test standard to measure the ability of private cloud services. The proposed method was implemented in Openstack. The experimental results show that private cloud platform service capacity can be obtained by the proposed method with lower cost and higher efficiency than user test. Compared with Openstack component Rally, scalability and dynamic load simulation of the proposed has greatly been improved.

With the rapid development and wide application of cloud storage system, many enterprise developers and individual users migrate their applications from traditional storage to public cloud storage system. Therefore, the performance of cloud storage system has become the focus of enterprise developers and individual users. The traditional test is difficult to simulate simultaneous access with enough users to the cloud storage system, complex to build and has a long test time with high cost. Besides, the evaluation results are unstable due to the network and other outside factors. In view of above critical problems, a kind of "cloud testing cloud" performance evaluation method was put forward for public cloud storage system. Public cloud storage system was evaluated by this method through applying a sufficient number of instances on the cloud computing platform. Firstly, a general performance evaluation framework was built with abilities such as dynamic instance application, automated deployment of assessment tools and load, controlling concurrent access to cloud storage system, automated instance release and evaluation results collection and feedback. Secondly, some multi-dimensional performance evaluation indicators were presented, covering different typical applications and different cloud storage interfaces. Finally, an extensible general performance evaluation model was put forward, which could evaluate the performance of typical applications, analyze the factors influencing cloud storage performance and be applied to any public cloud storage platform. In order to verify the feasibility, rationality, universality and expansibility of this method, these presented methods were applied to evaluate Amazon S3 cloud storage system, and then the accuracy of the evaluation results was verified by s3cmd. The results show that the evaluation output can provide reference comments for enterprise developers and individual users.