PANP-GM: A Periodic Adaptive Neighbor Workload Prediction Model Based on Grey Forecasting for Cloud Resource Provisioning

  • Yazhou HuEmail author
  • Bo Deng
  • Fuyang Peng
  • Dongxia Wang
  • Yu Yang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


Cloud computing platforms provide on-demand service to meet users’ need by adding or removing cloud resources dynamically. The cloud resource provisioning is often based on the feedback model, which causes time delay and resource wasters. Workload prediction methods can make the resource provisioning more instantaneous and reduce resource and power consumption, to meet service level objectives (SLOs) and improve quality of service (QoS) of cloud platform. In this paper, we propose a periodic adaptive neighbor workload prediction model based on grey forecasting (PANP-GM) for cloud resource provisioning. Firstly, the model analyzes the growth rate and evaluates the periodicity of workload. Secondly, this model uses the growth rate of previous neighbor periodicity to predicate the trend of upcoming workload. To adapt to dynamic changes and emergencies, the grey forecasting model is applied for automatic error correction and improving prediction accuracy. Experimental results demonstrate that PANP-GM can achieve better resource prediction accuracy than basic and general approaches. Furthermore, this model can effectively improve the QoS of cloud platform and reduce SLO violations.


Workload prediction Periodicity Grey forecasting model Resource provisioning 



This work is supported by the National High-Technology Research and Development Program of China (863 Program) (No. 2013AA01A215) and the National Natural Science Foundation of China (No. 61271252).


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Copyright information

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

Authors and Affiliations

  • Yazhou Hu
    • 1
    Email author
  • Bo Deng
    • 1
  • Fuyang Peng
    • 1
  • Dongxia Wang
    • 2
  • Yu Yang
    • 1
  1. 1.Beijing Institute of System EngineeringBeijingChina
  2. 2.National Key Laboratory of Science and Technology on Information System SecurityBeijingChina

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