A rapid and automatic deployment solution of cloud platform based on OpenStack was presented in order to improve OpenStack deployment efficiency. Firstly, the solution created image template files of different node types, and then replicated the image template by node types (such as network node, computing node), and according to the properties of the nodes (such as IP address, hostname tag), automatically modified the configuration file in the use of scripts in order to complete single node deployment. Then, the same strategy was used to achieve rapid deployment of other nodes. After that, the solution took advantage of network service (PXE(Preboot eXecute Environment), DHCP (Dynamic Host Configuration Protocol) and TFTP (Trivial File Transfer Protocol)) which were provided by management servers, mounted the image-block-file. Finally, nodes were started up to complete Dandelion. In addition, performance evaluation model was established to determine the optimal number of image copies and storage servers in order to optimize the storage network topology. Compared with other deployment schemes,such as Cobbler, NFS (Network File System), whether using the same size storage network to deploy different size cloud platforms, or using different size storage network to deploy the same size cloud platform, the experimental results show that the proposed solution can greatly reduce deployment time and improve efficiency of the deployment.

With the popularization of cloud computing technology, many enterprises have migrated or are planning to migrate their business and applications to the cloud. But it may face the problems of application performance degradation, and the key business and applications may suffer security threats. Therefore, migrating to cloud or deploying in independent server is a problem that needs to be further studied. In this paper, based on the Tsinghua cloud platform, Tsinghua cloud monitoring platform was set up based on Nagios. Firstly, Tsinghua cloud platform and its architecture were introduced, and then Nagios and the architecture of Tsinghua cloud monitoring platform were discussed. For cloud migration performance evaluation, Ubuntu and Windows were used as operating system platforms, CPU load and memory usage were used as evaluation indexes, two applications of CPU computing type and server load type respectively ran on the cloud server and the independent server. At last, the experimental results were analyzed and compared. The experimental result shows that some applications have better performance on independent servers that may not be suitable for migrating to cloud platform.

As the conventional physical cluster system fails to cope flexibly with large-scale Internet applications, a comprehensive load balancing mechanism for cloud-based virtual cluster system was proposed. It first periodically collected CPU and memory usage, number of connections, and response time of all virtual machines and physical hosts, then calculated the weighted load of the physical hosts, and finally scheduled and assigned the task requests based on the calculated comprehensive load, thus could adapt to the complex, dynamic and variable computing environment. The experimental results show that, compared with other scheduling mechanisms such as Weighted Round Robin (WRR) and Weighted Least Connections (WLC), the proposed mechanism is delay optimal under heavy workload, and moreover, it can increase or decrease the number of Virtual Machines (VMs) dynamically to balance the server load usually within 5 seconds.

Based on cloud computings architecture and the actual demands of Tsinghua University, followed by utilizing the advanced OpenStack platform, adopting hierarchical design method, the TsinghuaCloud platform that could be used to perform integrated management on cloud resources was designed and implemented. The advantages and main required module functions of this system were analyzed. Focusing on the resource scheduling, a strategy based on task scheduling and load balancing was proposed. The experiment and analysis of the scheduling plan verify that the scheduling strategy can balance servers resource load on the basis of ensuring its service performance and execution efficiency, so as to make the cloud platform relatively stable.