Real-time processing in client-server databases

In this paper, 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. Through a detailed performance scalability study, 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.