Visible to the public Virtual Machine Failure Prediction Method Based on AdaBoost-Hidden Markov Model

TitleVirtual Machine Failure Prediction Method Based on AdaBoost-Hidden Markov Model
Publication TypeConference Paper
Year of Publication2019
AuthorsLi, Zhixin, Liu, Lei, Kong, Degang
Conference Name2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS)
ISBN Number978-1-7281-1307-4
KeywordsAdaBoost-hidden Markov model, cloud computing, cloud platform environment, Failure Prediction, hidden Markov model, Hidden Markov models, hidden state, learning (artificial intelligence), observation state, Prediction algorithms, predictive ability, Predictive models, pubcrawl, security, software fault tolerance, Upper bound, virtual machine, virtual machine failure prediction method, virtual machine security, virtual machines, Virtual machining, VM failure state, VM security state

The failure prediction method of virtual machines (VM) guarantees reliability to cloud platforms. However, the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud platform. In this study, a failure prediction method of VM based on AdaBoost-Hidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud platforms. This method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model, proved the influence of the AdaBoost algorithm on the hidden Markov model (HMM), and realized the prediction of the VM failure state. Results show that the proposed method adapts to the complex dynamic cloud platform environment, can effectively predict the failure state of VMs, and improve the predictive ability of VM security state.

Citation Keyli_virtual_2019