Research on Virtual Machine Response Time Prediction Method Based on GA-BP Neural Network
Author(s) -
Jun Guo,
Shu Liu,
Bin Zhang,
Yongming Yan
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/141930
Subject(s) - virtual machine , response time , computer science , component (thermodynamics) , scalability , artificial neural network , genetic algorithm , virtual finite state machine , cloud computing , temporal isolation among virtual machines , quality of service , machine learning , distributed computing , artificial intelligence , operating system , computer network , virtualization , physics , thermodynamics
Cloud application provides access to large pool of virtual machines for building high-quality applications to satisfy customers’ requirements. A difficult issue is how to predict virtual machine response time because it determines when we could adjust dynamic scalable virtual machines. To address the critical issue, this paper proposes a prediction virtual machine response time method which is based on genetic algorithm-back propagation (GA-BP) neural network. First of all, we predict component response time by the past virtual machine component usage experience data: the number of concurrent requests and response time. Then, we could predict virtual machines service response time. The results of large-scale experiments show the effectiveness and feasibility of our method.
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