The contemporary soil analytical database processing techniques lack in optimization of databases and tables on storage grids, and are limited to the single instance of database transactions in handling large volumes of soil analytical data sets. Unfortunately, these scenarios increase data processing overheads in private agricultural cloud services. In this paper, we propose a Predictive Scalability Generator (PS-Gen) technique to optimize and partition the large data sets by creating indexed databases and tables dynamically on clustered storage grids. This intelligence is conceptualized by studying the k-means clustering algorithm which is highly used in cloud database processing systems. Our approach allows the creation of database and tables dynamically within a cluster by monitoring the cluster balancer defined in the system to handle large datasets. Alternatively, the proposed approach enables quick and dynamic movement of database and tables within clusters to manage load actively by performing the row-specific tuple management. This is achieved by integrating the horizontal sharding technique to our proposed method. The evaluated experimental results exhibit the effective management of large agricultural data in private cloud systems by effective load balancing across clusters. Further, the proposed approach is flexible for adopting network subsystems and to develop an efficient cloud-based application system.
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This work is supported and funded by the team of AICRP on STCR (Soil Test Crop Response), University of Agricultural Sciences (UAS), Gandhi Krishi Vignana Kendra (GKVK), Bangalore, Karnataka, India It is also financially supported by New Age Incubation Network (NAIN) ICT Skill Development Society, Department of IT, BT and S & T, Ref No: ICTSDS/CEO/17/2014-15, Govt. of Karnataka, and Vision Group on Science and Technology (VGST) scheme of RFTT, Govt. of Karnataka, Ref No: KSTePS/VGST-RFTT/2016-17/279/6.
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Leena, H.U., Premasudha, B.G. & Basavaraja, P.K. Data optimisation and partitioning in private cloud using dynamic clusters for agricultural datasets. Int. J. Dynam. Control 8, 1027–1039 (2020). https://doi.org/10.1007/s40435-019-00596-9
- Data optimization
- Private cloud
- Table partitioning