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Cluster Computing

Erschienen in:

12.03.2018

A new GLoSM embedded virtual machine model for big data services in cloud storage systems

verfasst von: K. Kalai Arasan, P. AnandhaKumar

Erschienen in: Cluster Computing | Sonderheft 1/2019

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Abstract

Big data and cloud are the common keystones of this data science era. The various social data contributes to the overall data traffic of the universe. The data management policies have gained much interest and research community is moving towards it. Cloud storage is one of such methodology used to manage big data elements. Due to centralized issues in job scheduling, the VMs are used to schedule the jobs. Waiting for service reply from a specific VM or delivering to a mass community from a far apart VM improves the cost of data traffic and completion time. We have proposed LoSM and GLoSM to satisfy the service request from ‘n’ number of hosts with respect to proximity and service activity. The test results from CloudSim and HDFS show that the proposed system outperforms with minimal completion time, reduced data traffic and less energy consumption.

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Titel: A new GLoSM embedded virtual machine model for big data services in cloud storage systems
verfasst von: K. Kalai Arasan
P. AnandhaKumar
Publikationsdatum: 12.03.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 1/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-2109-z

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