Dynamic Scheduling Method of Virtual Resources Based on the Prediction Model

  • Dongju YangEmail author
  • Chongbin Deng
  • Zhuofeng Zhao
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


Deploying applications to the cloud has become an increasingly popular way in the industry due to elasticity and flexibility. It uses virtualization technology to provide storing and computing resources to the applications. So how to efficiently schedule virtual resources to ensure the quality of services during the peak, and avoid the waste of resources during the idle is an important research topic in the cloud computing, which aims to minimize the execution cost and to increase the resource utilization. The way based on the monitoring data to scale up or scale down the virtual resources may let virtual resources suffer from over seriously. In this paper, we present a dynamic scheduling method for the virtual resources based on the prediction model. Firstly, we use prediction model to predict the request quantity. And then we combined the prediction result with the load capacity of current resources to compute whether to increase or decrease the virtual resources. Finally, we choose the suitable physical machine to create or recycle the virtual machine. The experimental results show that the prediction model can fit our scene well, and the resource scheduling algorithm can be used to ensure the quality of service in a timely and effective manner.


Cloud application Surge in traffic Quality of service Prediction model Dynamic resource scheduling 



This work is supported by Key Program of Beijing Municipal Natural Science Foundation “Theory and Key Technologies of Data Space Towards Large Scale Stream Data Processing” (No. 4131001).


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, Research Center for Cloud ComputingNorth China University of TechnologyBeijingChina

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