Evolutionary Algorithms for Allocating Data in Distributed Database Systems

  • Authors:

  • Ishfaq Ahmad;Kamalakar Karlapalem;Yu-Kwong Kwok;Siu-Kai So

  • Affiliations:

  • Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, People's Republic of China. iahmad@cs.ust.hk;Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, People's Republic of China. kamal@cs.ust.hk;Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, People's Republic of China. ykwok@eee.hku.hk;Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, People's Republic of China. kai@cs.ust.hk

  • Venue:
  • Distributed and Parallel Databases
  • Year:
  • 2002

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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.