Data Allocation in Distributed Database based on CSO

Abstract

Distributed databases (DDBs) provide smart processing of large databases, the problems of fragmentation and allocation are vital design problems in addition to the centralized design. The majority of performance degradation in DDBs is due to the communication cost by query remote access and retrieval of data. This can be optimized through an efficient data allocation approach that will provide flexible retrieval of a query by low cost accessible sites. In this paper, a novel high performance data allocation approach is designed using Chicken Swarm Optimization (CSO) algorithm. Data allocation problem (DAP) is a NP-Hard problem modelled as optimization problem. The proposed data allocation approach initially characterizes the DAP into optimal problem of choosing the appropriate and minimal communication cost provoking sites for the data fragments. Then the CSO algorithm optimally chooses the sites for each of the data fragments without creating much overhead and data route diversions. This enhances the overall distributed database design and subsequently ensures quality replication. The experimental results illustrate that the proposed CSO based intelligent data fragment allocation approach has better performance than most existing approaches and thus signifies the impact of efficient data allocation in DDBs.