Static and adaptive distributed data replication using genetic algorithms

• Published in: Journal of parallel and distributed computing Vol. 64; no. 11; pp. 1270 - 1285
• Main Authors: Loukopoulos, Thanasis, Ahmad, Ishfaq
• Format: Journal Article
• Language: English
• Published: San Diego, CA Elsevier Inc 01-11-2004; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data replication; Exact sciences and technology; Genetic algorithms; Greedy method; Internet; Problem solving, game playing; Software; Static and dynamic allocation; Telecommunications; Telecommunications and information theory; Teleprocessing networks. Isdn; World wide web; Static and dynamic allocation; Data replication; Internet; Greedy method; World wide web; Genetic algorithms; Worldwide web; Static allocation; Distributed database; Constraint satisfaction; Information access; Distributed system; Information dissemination; Dynamic allocation; Genetic algorithm; Problem solving
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2004.04.005

Fast dissemination and access of information in large distributed systems, such as the Internet, has become a norm of our daily life. However, undesired long delays experienced by end-users, especially during the peak hours, continue to be a common problem. Replicating some of the objects at multiple sites is one possible solution in decreasing network traffic. The decision of what to replicate where, requires solving a constraint optimization problem which is NP-complete in general. Such problems are known to stretch the capacity of a Genetic Algorithm (GA) to its limits. Nevertheless, we propose a GA to solve the problem when the read/write demands remain static and experimentally prove the superior solution quality obtained compared to an intuitive greedy method. Unfortunately, the static GA approach involves high running time and may not be useful when read/write demands continuously change, as is the case with breaking news. To tackle such case we propose a hybrid GA that takes as input the current replica distribution and computes a new one using knowledge about the network attributes and the changes occurred. Keeping in view more pragmatic scenarios in today's distributed information environments, we evaluate these algorithms with respect to the storage capacity constraint of each site as well as variations in the popularity of objects, and also examine the trade-off between running time and solution quality.