A Novel Fragmentation Scheme for Textual Data Using Similarity-Based Threshold Segmentation Method in Distributed Network Environment

Tarun, Sashi; Batth, Ranbir Singh; Kaur, Sukhpreet

doi:10.22247/ijcna/2020/205322

Cited by 1 publication

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“…The success of a distributed system is determined by how data is fragmented and distributed across several geographical locations [1], [2]. One of the most difficult aspects of distributed architecture is overcoming the growing workforce load and cutting down data allocation costs.…”

Section: Introductionmentioning

confidence: 99%

An optimized cost-based data allocation model for heterogeneous distributed computing systems

Tarun

Dubey

Batth

et al. 2022

IJECE

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<span lang="EN-US">Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.</span>

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Section: Introductionmentioning

confidence: 99%