TASM: technocrat ARIMA and SVR model for workload prediction of web applications in cloud

  • Parminder Singh
  • Pooja Gupta
  • Kiran Jyoti


Workload patterns of cloud applications are changing regularly. The workload prediction model is key for auto-scaling of resources in a cloud environment. It is helping with cost reduction and efficient resource utilization. The workload for the web applications is usually mixed for different application at different time span. The single prediction model is not able to predict different kinds of workload pattern of cloud applications. In this paper, an adaptive prediction model has been proposed using linear regression, ARIMA, and support vector regression for web applications. Workload classifier has been proposed to select the model as per workload features. Further the model parameters are selected through a heuristic approach. We have used real trace files to evaluate the proposed model with existing state-of-the-art models. The experiment results describe the significant improvement in root-mean-squared error and mean absolute percentage error metrics, and improve the quality of service of web applications in a cloud environment.


Workload prediction Web applications Cloud computing Resource provisioning Elasticity 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Lovely Professional UniversityJalandharIndia
  2. 2.Guru Nanak Dev Engineering CollegeLudhianaIndia

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