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A new ant colony optimization based algorithm for data allocation problem in distributed databases

机译:一种新的基于蚁群优化的分布式数据库数据分配算法

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The Performance and the efficiency of a distributed database system depend highly on the way data are allocated to the sites. The NP-completeness of the data allocation problem and the large size of its real occurrence, call for employing a fast and scalable heuristic algorithm. In this paper, we address the data allocation problem in terms of minimizing two different types of data transmission across the network, i.e., data transmissions due to site-fragment dependencies and those caused by inter-fragment dependencies. We propose a new heuristic algorithm which is based on the ant colony optimization meta-heuristic, with regards to the applied strategies for query optimization and integrity enforcement. The goal is to design an efficient data allocation scheme to minimize the total transaction response time under memory capacity constraints of the sites. Experimental tests indicate that our algorithm is capable of producing near- optimal solutions within a reasonable time. The results also reveal the flexibility and scalability of the proposed algorithm.
机译:分布式数据库系统的性能和效率在很大程度上取决于将数据分配给站点的方式。数据分配问题的NP完备性及其实际发生的规模大,要求采用快速且可扩展的启发式算法。在本文中,我们通过最小化两种不同类型的数据在网络上的传输来解决数据分配问题,即两种数据传输是由于站点碎片依赖性和碎片间依赖性引起的。针对查询优化和完整性执行的应用策略,我们提出了一种基于蚁群优化元启发式算法的启发式算法。目标是设计一种有效的数据分配方案,以在站点的内存容量限制下将总事务响应时间最小化。实验测试表明,我们的算法能够在合理的时间内产生接近最佳的解决方案。结果还揭示了所提出算法的灵活性和可扩展性。

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