CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
An adaptive data replication algorithm
ACM Transactions on Database Systems (TODS)
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Five Steps to Construct a Model of Data Allocation for Distributed Database Systems
Journal of Intelligent Information Systems
Evolutionary Algorithms for Allocating Data in Distributed Database Systems
Distributed and Parallel Databases
Introduction to Algorithms
Scheduling Data Redistribution in Distributed Databases
Proceedings of the Sixth International Conference on Data Engineering
Allocating Fragments in Distributed Databases
IEEE Transactions on Parallel and Distributed Systems
International Journal of Computers and Applications
A new ant colony optimization based algorithm for data allocation problem in distributed databases
Knowledge and Information Systems
DYFRAM: dynamic fragmentation and replica management in distributed database systems
Distributed and Parallel Databases
An Efficient Approach for Data Placement in Distributed Systems
MUE '11 Proceedings of the 2011 Fifth FTRA International Conference on Multimedia and Ubiquitous Engineering
A genetic algorithm-based clustering approach for database partitioning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Database fragmentation and allocation: an integrated methodology and case study
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
Proper data allocation is a key performance factor for an efficient functionality of Distributed Database Systems (DDBSs). Therefore, if data allocation across sites is performed accurately while preserving the issues of communication and site constraints, an optimal solution for DDBS performance in a dynamic distributed environment will be achieved. In this paper, a new dynamic data allocation algorithm for non-replicated DDBS is presented, the proposed Performance Optimality Enhancement Algorithm (POEA) explores and improves some concepts used in previously developed algorithms to reallocates fragments to different sites given the changing data access patterns, time and sites constraints of the DDBS. Moreover, the POEA adopts the shortest path between the old location and the new anticipated location for the transferred fragments when migration decision is made. Experimental results show that POEA has efficiently reduced the transmission cost subsequently minimizing the frequency and the time spent on fragment migration over the network sites resulting to a great improvement in the overall DDBS performance.