Towards an Efficient Data Fragmentation, Allocation, and Clustering Approach in a Distributed Environment

Abstract

Data fragmentation and allocation has for long proven to be an efficient technique for improving the performance of distributed database systems’ (DDBSs). A crucial feature of any successful DDBS design revolves around placing an intrinsic emphasis on minimizing transmission costs (TC). This work; therefore, focuses on improving distribution performance based on transmission cost minimization. To do so, data fragmentation and allocation techniques are utilized in this work along with investigating several data replication scenarios. Moreover, site clustering is leveraged with the aim of producing a minimum possible number of highly balanced clusters. By doing so, TC is proved to be immensely reduced, as depicted in performance evaluation. DDBS performance is measured using TC objective function. An inclusive evaluation has been made in a simulated environment, and the compared results have demonstrated the superiority and efficacy of the proposed approach on reducing TC.