Matteo Stefanini, Riccardo Lancellotti, Lorenzo Baraldi, Simone Calderara
Proceeding of the International Conference on Cloud Computing and Services Science 2019
March 2019
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Abstract
Cloud computing data centers are growing in size and complexity to the point where monitoring and management of the infrastructure become a challenge due to scalability issues. A possible approach to cope with the size of such data centers is to identify VMs exhibiting a similar behavior. Existing literature demonstrated that clustering together VMs that show a similar behavior may improve the scalability of both monitoring and management of a data center. However, available techniques suffer from a trade-off between accuracy and time to achieve this result. Throughout this paper we propose a different approach where, instead of an unsupervised clustering, we rely on classifiers based on deep learning techniques to assign a newly deployed VMs to a cluster of already-known VMs. The two proposed classifiers, namely DeepConv and DeepFFT use a convolution neural network and (in the latter model) exploits Fast Fourier Transform to classify the VMs. Our proposal is validated using a set of traces describing the behavior of VMs from a real cloud data center. The experiments compare our proposal with state-of-the-art solutions available in literature, demonstrating that our proposal achieve better performance. Furthermore, we show that our solution is significantly faster than the alternatives as it can produce a perfect classification even with just a few samples of data, making our proposal viable also to classify on-demand VMs that are characterized by a short life span.
Type: Conference Paper
Publication: International Conference on Cloud Computing and Services Science 2019
Full Paper: link pdf
Please cite with the following BibTeX:
@article{stefanini2019deep,
title={A Deep Learning based approach to VM behavior identification in cloud systems},
author={Stefanini, Matteo and Lancellotti, Riccardo and Baraldi, Lorenzo and Calderara, Simone},
journal={arXiv preprint arXiv:1903.01930},
year={2019}
}