Given a distributed multimedia database system and a set of queries as well as their frequencies from each site, the objective of a data allocation algorithm is to locate the multimedia data objects (MDOs) at different sites so as to minimize the total data transfer cost incurred in executing the queries. The data allocation problem, however, is NP-complete, and thus requires fast heuristics to generate efficient solutions. In this paper we propose three data allocation algorithms which are based on a genetic technique, an evolutionary process, and neural networks. We have implemented and evaluated these algorithms on our distributed multimedia database system test-bed. A comparison of the algorithms reveals trade-offs between their solution quality and time-complexity. 1

On a Wide Area Network (WAN), fragment allocation is a major issue in distributed database design since it concerns the overall performance of distributed database systems. Here we propose a simple and comprehensive model that reflects transaction behavior in distributed databases. Based on the model and transaction information, two heuristic algorithms are developed to find a near-optimal allocation such that the total communication cost is minimized as much as possible. The results show that the fragment allocation found by the algorithms is close to being an optimal one. Some experiments were also conducted to verify that the cost formulas can truly reflect the communication cost in the real world.

Abstract. A major cost in executing queries in a distributed database system is the data transfer cost incurred in transferring relations (fragments) accessed by a query from different sites to the site where the query is initiated. The objective of a data allocation algorithm is to determine an assignment of fragments at different sites so as to minimize the total data transfer cost incurred in executing a set of queries. This is equivalent to minimizing the average query execution time, which is of primary importance in a wide class of distributed conventional as well as multimedia database systems. The data allocation problem, however, is NP-complete, and thus requires fast heuristics to generate efficient solutions. Furthermore, the optimal allocation of database objects highly depends on the query execution strategy employed by a distributed database system, and the given query execution strategy usually assumes an allocation of the fragments. We develop a site-independent fragment dependency graph representation to model the dependencies among the fragments accessed by a query, and use it to formulate and tackle data allocation problems for distributed database systems based on query-site and move-small query execution strategies. We have designed and evaluated evolutionary algorithms for data allocation for distributed database systems.

A major cost in executing queries in a distributed database system is the data transfer cost incurred in transferring multiple database objects (fragments) accessed by a query from different sites to the site where the query is initiated. The objective of a data allocation algorithm is to locate the fragments at different sites so as to minimize the total data transfer cost incurred in executing a set of queries. We develop a site-independent fragment dependency graph representation to model the dependencies among the fragments accessed by a query, and use it to formulate and solve data allocation problems for distributed database systems based on (query-site and move-small) query execution strategies. We show that an optimal solution can be achieved when the query-site query execution strategy is employed, and for the move-small query execution strategy we performed experimental evaluation about the effectiveness of a hillclimbing heuristic algorithm in achieving a near-optimal soluti...

This work addresses a files allocation problem (FAP) in distributed computing systems. This FAP attempts to minimize the expected data transfer time for a specific program that must access several data files from non-perfect computer sites. We assume that communication capacity can be reserved; hence, the data transmission behavior is modeled as a many-to-one multi-commodity flow problem. A new critical-cut method is proposed to solve this reduced multi-commodity flow problem. Based on this method, two algorithms which use branch-and-bound are proposed for this FAP. The proposed algorithms are able to allocate data files having single copies or multiple replicated copies. Simulation results are presented to demonstrate the performance of the algorithms.

The problem of allocating high-volume multimedia files on a virtual circuit network with the objective of maximizing channel throughput (and minimizing data transmission time) is addressed. The problem is formulated as a multicommodity flow problem. We present both the optimal and suboptimal solutions to the problem using novel approaches.