A Deep Learning based approach to VM behavior identification in cloud systems

TL;DR

This paper introduces deep learning classifiers, DeepConv and DeepFFT, to identify VM behavior in cloud data centers, offering improved accuracy and speed over existing clustering methods, especially for short-lived VMs.

Contribution

The paper presents a novel deep learning approach for VM behavior classification that outperforms state-of-the-art methods in accuracy and speed, suitable for on-demand VM classification.

Findings

Deep learning classifiers outperform existing methods in accuracy.

Proposed models classify VMs quickly even with limited data.

Method is effective for short-lived VMs in cloud environments.

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 andmanagement 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 assigna 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 Transformation to classify the VMs. Our proposal is validated using a set of traces describing the behavior of VMs from a realcloud 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 issignificantly faster than the alternatives as it can produce a perfect classification even with just a few samples of data, making our proposal viable also toclassify on-demand VMs that are characterized by a short life span.