Clustering of Client-Sites in Three-Tier Database Architectures

Conventional two-tier databases have shown performance limitations in the presence of many concurrent clients. We propose logical grouping of clients (or clustering) as the means to improve the performance of collaborative networked databases. In this paper, we discuss a three-tier client-server database architecture (3t-CSD) featuring the above partitioning. The proposed clustering is based on the similarity of clients’ access patterns. Each cluster is supervised by a designated manager that coordinates data sharing among its members. A number of clients is optimally partitioned if sites in each individual cluster have the maximum common data access probability possible. We initially show that the optimal client clustering problem is NP-complete and then we develop two approximate solutions based on abstraction and filtering of statistics for client access patterns. Our main goal is to compare the performance of the conventional and three-tier client-server database architecture with respect to the transaction turnaround times and object response times. After developing system prototypes that implement both two-tier and 3t-CSDs, we experimentally show that as long as good client-clustering is possible, the 3t-CSD architecture yields sizable gains over its conventional counterpart. We also compare and evaluate the effectiveness of the two proposed techniques used to create client clusters. Finally, we examine the role of several preprocessing schemes used to reduce the volume of the input data supplied to the clustering techniques.