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Workload Forecasting Based on Big Data Characteristics in Cloud Systems

  • R. Kiruthiga
  • D. Akila
Conference paper
  • 25 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 118)

Abstract

Resource allocation for big data streams in cloud systems involves selecting the appropriate cloud resources. Big data has certain precise features such as size, speed, veracity, variety, and value. In this paper, a workload forecasting system for resource allocation in big data streams is developed. In this system, the data characteristics such as data type (variety), size (volume), and deviation in data flow rate (velocity) are extracted. Based on these data characteristics, the expected workload of the next time interval is predicted using support vector machine (SVM). Followed by this, the cloud resource manager dynamically allocates the available cloud resources depending on the predicted workload. The presentation outcomes have confirmed that the proposed system has less execution time and achieves better utilization of resources, when compared to the existing tools.

Keywords

Big data Resource allocation SVM Workload forecast 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • R. Kiruthiga
    • 1
  • D. Akila
    • 1
  1. 1.School of Computing SciencesVels Institute of Science, Technology & Advanced Studies (VISTAS)ChennaiIndia

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