The Effects of Parallel Processing on Update Response Time in Distributed Database Design

Network latency and local update are the most significant components of update response time in a distributed database system. Effectively designed distributed database systems can take advantage of parallel processing to minimize this time. We present a design approach to response time minimization for update transactions in a distributed database. Response time is calculated as the sum of local processing and communication, including transmit time, queuing delays, and network latency. We demonstrate that parallelism has significant impacts on the efficiency of data allocation strategies in the design of high transaction-volume distributed databases

A Decision Support Tool for Distributed Database Design

The efficiency and effectiveness of a distributed database depend primarily on solving two interrelated design problems: data allocation, specifying what data to replicate and where to store it, and operating strategies, specifying where and how retrieval and update processes are performed. We develop a distributed database design approach that comprehensively addresses these problems, explicitly modeling their interdependencies for both retrieval and update processing. We extend earlier distributed database design models to include join order and data reduction by semijoin, in addition to data replication, copy identification, and join node selection. We demonstrate that join ordering and data reduction by semijoin are important distributed database design decisions that must be included in a distributed database design algorithm if it is to determine an overall optimal distributed database design

Designing Distributed Database Systems for Efficient Operation

Distributed database systems can yield significant cost and performance advantages over centralized systems for geographically distributed organizations. The efficiency of a distributed database depends primarily on the data allocation (data replication and placement) and the operating strategies (where and how retrieval and update query processing operations are performed). We develop a distributed database design approach that comprehensively treats data allocation and operating strategies, explicitly modeling their interdependencies for both retrieval and updateprocessing. Wedemonstratethatdatareplication,joinnodeselection,anddatareductionbysemijoinare important design and operating decisions that have significant impact on both the cost and response time of a distributed database system