The fast growing bandwidth has made the development of cloud storage. More and more resource has put in cloud storage. In this thesis, we proposed a new cloud storage that consists of a single main server and multiple data servers. The main server controls system-wide activities such as data server management. It also periodically communicates with each data server and collects its state. Data servers store data on local disks as Windows files. In order to response to the large number of data access, Selection of the server which is necessary to offer equalized performance. In this paper, we propose a server selection algorithm using different parameters to get the performance metrics which enables us to balance multi-resource from server-side. We design new cloud storage and implement the algorithm. According to upload experiment, the difference between the maximum and the minimum free space when using our algorithm is less than 5GB. But using the random mode, the free space difference is increased as time, and the maximum is 30GB. In the mixed experiment, we added the download mode, and our algorithm is fewer than 10GB. The result of the random mode approximated to the first experiment. Finally, our algorithm obtains 10% and 3% speedup in upload throughput by upload experiment and mixed experiment, 10% speedup in download throughput by mixed experiment.

The popularity of the World Wide Web (WWW) in providing a vast array of information has drawn a large number of users in the past few years. The dramatic increase in the number of Internet users, however, has brought undesirable impacts on users, such as long response time and service unavailability. The utilization of multiple servers can be used to reduce adverse impacts. The challenge is to identify a good resource site to allocate to the user given a group of servers from which to select. In this project, a performance-directed site selection system was developed for a web-based application called AADMLSS (African American Distributed Multiple Learning Styles System). Four different sets of experiments were conducted in this study. In order to evaluate the effectiveness of the test system, two other server selection methods, Load-based and Random-based methods, were implemented for comparative purposes. The experiments were also run during daytime and nighttime to see the impact of network load on the response time. Experimental results indicate that the performance-directed site selection system outperforms the Load-based and Random-based methods consistently. The response time is typically high during daytime and low during nighttime, indicating that the network load has an impact on the response time delivered. The results also show that server performance contributes to the overall response time, and network performance is the more dominating factor in determining a good resource site for the user.

No description available.