REPLICATION & DISTRIBUTED DATABASE

Replication & Distributed Database

Abstract

A distributed database system (DDS) system consists of multiple server nodes interconnected by a metropolitan area network (MAN) or a wide area network (WAN). It can achieve better efficiency in handling ever-increasing web requests than centralized distributed databases because its throughput will not be limited by available bandwidth connecting to a central server. The key research issue in the design of DDS is how to replicate and distribute the documents of a website among the server nodes. This paper proposes a density-based replication scheme and applies it to our proposed Extensible DDS architecture. We adopted a partial duplication scheme where document replication targets only at hot objects in a website. To distribute the replicas generated via the density-based replication scheme, we propose four different document distribution algorithms: Greedy-cost, Maximal-density, Greedy-penalty, and Proximity-aware. A proximity-based routing mechanism is designed to incorporate these algorithms for achieving better distributed database performance in a WAN environment. Simulation results show that the Greedy-penalty algorithm yields most stable load-balancing performance, and the Greedy-cost algorithm causes least internal traffic. Our scheme can achieve 80% of the performance of full-replication, with half the disk space.

Introduction

This research question covered in this paper is; Data replication in distributed databases, which has to do with replication of data and data handling processes such as queries may form and unprecedented huddle especially with the advancement of internet technologies and distributed computing. The thesis of the paper states: Data replication affects distributed databases uniquely stores data at different locations and processes them at these distributed channels and nodes thus forming the element of reliability, availability of data, faster speeds in processing and querying data and also fosters sharing of data. Some of the significant challenges include the high cost of maintenance, complexity in data processing, the vulnerability of attacks to unique nodes within the system and mainly, the replication of data.

Distributed databases thrive on the fact that there is need to decentralize and distribute data handling within the systems. This however causes undue replication of data whereby the system maintains several copies of a relation, each at a different site or node location. It has its comparative advantages such as the quick availability of data when and if needed, and the fact that processes on a specified data can be done concurrently at different sites or code location within the distributed system. The disadvantages tend to further overwhelm the advantages in that the system would incur extra time in update of queries and each node location. That is to mean that after the processes at each point or node are done, the system would then have to update the unique changes effected at each node.

Moreover, the overhead tasks of querying each node would and might at times result into data collision and inconsistencies. This paper therefore endeavors to overcome replication in distributed database systems and offers practical solutions that touch the design and development of such ...