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.

Upper bounds on the scalability of client-server databases (CSDs) persist due to long delays experienced at the servers' queues. To this end, we have proposed a three-tier architecture that exploits similarities in object access behavior demonstrated by clients. Clients are statically grouped into logical clusters and object requests can be then served in an "internal to cluster" fashion. This is achieved with the introduction of an intermediate directory tier. Good client clustering yields more scalable CSD configurations as it minimizes the number of inter-cluster data accesses. We introduce the problem of client realignment in light of changing client localities and propose an online reclustering framework to address it. Online reclustering facilitates adaptive reconfiguration and redistribution of sites. The core of our proposal is a change detection approach that uses meta-data extracted from observed clients' access patterns. We evaluate the impact of employing a multi-featured change detection scheme in the three-tier CSD architecture and experimentally investigate its performance and trade-offs involved.

In conventional two-tier client-server databases, clients access and modify shared data resident in a common server. As the number of clients increases, the centralized database server can become a performance bottleneck. In order to overcome this scalability problem, a three-tier client-server configuration has been proposed that features the partitioning of clients into logical clusters. Here, the objective is to maximize the data sharing among the clients in each cluster. We propose a genetic algorithm to create such client clusters and evaluate two different techniques for generating the initial solution populations. We compare the performance of the two-tier and three-tier configurations with respect to the transaction turnaround times and object response times. Our experimental results indicate that the clustered architecture can offer improved performance over its two-tier counterpart.

Computer systems deployed in contemporary industrial and production environments typically involve a large number of workers who work simultaneously to draft new components and/or trace provided services. Efficient database support for such systems is crucial. The handling of high volume data among various sites of a network based infrastructure poses new challenges as it can be only carried out by aggregates of data servers and powerful workstations/PCs (clients). Operating in such environments, client/server databases (C/S DBSs) (M. Carey et al., 1991) have shown reduced response times for client transactions over their centralized counterparts. Previous work has indicated, however, that when the number of clients attached per server becomes large, C/S DBSs fail to guarantee satisfactory performance rates. This is known as the scalability problem. Client object caching is used as a mechanism to improve system scalability by easing resource contention at the server sites. Design and manufacturing databases display different operational characteristics from their conventional counterparts as they routinely support multiple and mostly independent projects. In such settings, a group of cooperating designers or workers achieve a complex task by closely interacting among themselves and dynamically sharing design data. We exploit the aforementioned feature of design and manufacturing databases, and propose a cluster based caching configuration. The cluster based configuration groups sites according to the similarity of their access patterns. This results in improved client performance rates and offers better scalability.