A Taxonomy and Survey of Data Partitioning Algorithms for Big Data Distributed Systems

Waseem, Quadri; Maarof, Mohd Aizaini; Idris, Mohd. Yazid; Nazir, Amril

doi:10.1007/978-981-15-2329-8_46

Cited by 3 publications

(1 citation statement)

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“…Data partitioning is the process of splitting the large dataset into equivalent separate blocks of data for data scaling. This process is the necessary one for improved performance for large workloads [1]. The major benefit of data partitioning is storing large data based on their category [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel HVgP and OLBP approach in Distributed Transaction Partitioning

Bharati

Attar

2022

Preprint

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Data partitioning acts an important role in Online Transaction Processing (OLTP) to enhance their performance and scalability. Partitioning is important in OLTP based systems due to the increase of user populations and online transactions. Nowadays, providing scalable and reliable data organization is the main aim of database researchers. This work presented an innovative data partitioning approach and optimized load balancing for scalable transactions. Initially, the Hybrid Vertical-graph partitioning (HVgP) approach is utilized for appropriate data partitioning. After the process of data partitioning, the partitions efficiency and the average workload parameters are computed and their corresponding weight factor is computed. Subsequently, Optimized Improved Load Balancing (OLBP) algorithm is utilized for scalable OLTP data transactions on distributed database. Here, the estimated weight factor is updated for effective load balancing with data partitions. The presented approach is appropriate for various online data transaction applications. The quality of the presented approach is examined using TPC-E OLTP benchmark dataset. The performance of the improved approach is examined with the different existing approaches and proved the significant enhancement in different performance metrics like Throughput, Response time, distributed transactions, and CPU utilization.

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Section: Introductionmentioning

confidence: 99%