دانلود رایگان ترجمه مقاله مطالعه موردی طراحی پایگاه داده توزیع شده (نشریه الزویر 2014)

مطالعه موردی طراحی پایگاه داده توزیع شده

Distributed Database Design: A Case Study

Abstract

Data Allocation is an important problem in Distributed Database Design. Generally, evolutionary algorithms are used to determine the assignments of fragments to sites. Data Allocation Algorithms should handle replication, query frequencies, quality of service (QoS), cite capacities, table update costs, selection and projection costs. Most of the algorithms in the literature attack one or few components of the problem. In this paper, we present a case study considering all of these features. The proposed model uses Integer Linear Programming for the formulation of the problem.

1 Introduction

Query response times, quality of service (QoS), consistency and integrity of data are very important in Distributed Database Management System (DDBMS) applications. In a DDBMS, tables and fragments are distributed on different sites. Each query is executed from a site. The total cost consists of the cost the of query plan execution and the cost of table/fragment accesses through the network. The data allocation problem is NP-complete. Therefore, evolutionary algorithms are generally used to find a minimal cost solution to the problem. Data allocation algorithms try to minimize the table/fragment access cost of the queries. They find an optimal allocation of tables/fragments to sites. They also consider parameters like redundant data, table update costs, and site capacities. There are several factors to be considered when designing a DDBMS. The queries deployed may have shared tasks and same queries may originate from different sites. Site capacities, processing elements, storage and query response times are to be handled at the same time. Therefore, the problem shows the nature of a multi objective optimization problem. We designed a model with Integer Linear Programming (ILP) which has the capability of issuing each of these factors as constraints. Network topology, replication, table/fragment update costs, originating sites, site capacities, query frequencies can all be defined as constraints in this formulation.