Non-redundant Distributed Database Allocation Technology Research

Sun, Qiao; Deng, Buqiao; Fu, Lan-Mei; Sun, Jiasong

doi:10.1109/ciis.2017.57

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…This approach estimates the cost of reallocation and then utilizes the highest query update cost site for migration decision with minimal cost for communication. Sun et al 2017, [16] suggested a dynamic nonredundant data allocation approach for distributed database systems, in which fragment update parameters and dynamic cost parameters are specified in order to discover the optimal solution for reallocating redundant data. This non-redundant approach takes into account the subsystem's time to allocate jobs in order to tackle the challenge of determining the source of unreliability in reliability task assignment.…”

Section: Related Workmentioning

confidence: 99%

Data Allocation in Distributed Database based on CSO

Thalij¹

2022

Tikrit J. Pure Sci.

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Distributed databases (DDBs) provide smart processing of large databases, the problems of fragmentation and allocation are vital design problems in addition to the centralized design. The majority of performance degradation in DDBs is due to the communication cost by query remote access and retrieval of data. This can be optimized through an efficient data allocation approach that will provide flexible retrieval of a query by low cost accessible sites. In this paper, a novel high performance data allocation approach is designed using Chicken Swarm Optimization (CSO) algorithm. Data allocation problem (DAP) is a NP-Hard problem modelled as optimization problem. The proposed data allocation approach initially characterizes the DAP into optimal problem of choosing the appropriate and minimal communication cost provoking sites for the data fragments. Then the CSO algorithm optimally chooses the sites for each of the data fragments without creating much overhead and data route diversions. This enhances the overall distributed database design and subsequently ensures quality replication. The experimental results illustrate that the proposed CSO based intelligent data fragment allocation approach has better performance than most existing approaches and thus signifies the impact of efficient data allocation in DDBs.

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Section: Related Workmentioning

confidence: 99%

Data Allocation in Distributed Database based on CSO

Thalij¹

2022

Tikrit J. Pure Sci.

View full text Add to dashboard Cite

show abstract

Non-redundant Distributed Database Allocation Technology Research

Cited by 1 publication

References 9 publications

Data Allocation in Distributed Database based on CSO

Data Allocation in Distributed Database based on CSO

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