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.

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.