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.

We propose and experimentally evaluate the use of the client-server database paradigm for real-time processing. To date, the study of transaction processing with time constraints has mostly been restricted to centralized or "single-node" systems. Recently, client-server databases have exploited locality of data accesses in real-world applications to successfully provide reduced transaction response times. Our objective is to investigate the feasibility of real-time processing in a data-shipping client-server database architecture. We compare the efficiency of the proposed architecture with that of a centralized real-time database system. We discuss transaction scheduling issues in the two architectures and propose a new policy for scheduling transactions in the client-server environment. This policy assigns higher priorities to transactions that have a greater potential for successful completion through the use of locally available data. We investigate the effects of client data-access locality and various updating workloads on transaction completion rates. Our experimental results show that real-time client-server databases can provide significant performance gains over their centralized counterparts. These gains become evident when large numbers of clients (more than 40) are attached per server, even in the presence of high data contention.

In a conventional client-server database system (CS-DBS), a transaction and its requisite data have to be colocated at a single site for the operation to proceed. This has traditionally been achieved by moving either the data or the transaction. Today, the availability of powerful workstations and high-bandwidth networking options has led users to expect real-time guarantees about the completion times of their tasks. So as to offer such guarantees in a CS-DBS, a transaction should be processed by any means that allows it to meet its deadline. In this paper we explore the option of moving both transactions and data to the most promising sites for successful completion. We propose a load-sharing mechanism that oversees the shipment of data and transactions in order to increase the efficiency of a client-server cluster. Additionally, we make use of the concept of grouped locks to schedule the movement of data objects in the cluster in a more efficient manner. An experimental evaluation shows that the use of our load-sharing algorithm provides a considerable improvement in the real-time processing efficiency of a CS-DBS even in the presence of very high volumes of update transactions.

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.

Shows that there is scope for replication in data-shipping client-server DBMSs, offering opportunities for improved transaction response times. To support this replication, we describe a two-stage protocol for transaction processing (2STP). We extend the conventional client-server data-shipping mechanism by allowing clients to update and query cached objects that have replicas in multiple sites. We use the concept of acceptance criteria to provide a means for the flexible handling of client updates. The effectiveness of the two-stage transaction processing mechanism is supported by means of queuing analysis and detailed simulation experiments comparing 2STP with a global lock-based data-shipping protocol. This improvement in transaction processing efficiency is achieved at the cost of longer downtimes for crash recovery.