Detecting performance degradation in cloud systems using LSTM autoencoders

Chouliaras, Spyridon and Sotiriadis, Stelios (2021) Detecting performance degradation in cloud systems using LSTM autoencoders. In: Barolli, L. and Woungang, I. and Enokido, T. (eds.) Advanced Information Networking and Applications: Proceedings of the 35th International Conference on Advanced Information Networking and Applications (AINA-2021). Lecture Notes in Networks and Systems 2 226. Springer, pp. 472-481. ISBN 9783030750749.

Abstract

Cloud computing technology is on the rise as it provides an easy to scale environment for Internet users in terms of computational resources. At the same time, cloud providers manage this demand for computational power by offering a pay per use model for virtualized resources. Yet, it is a challenging issue to administer the variety of different cloud applications and ensure high performance by identifying failures and errors on runtime. Distributed applications are error-prone, and creating a platform to support minimum hardware and software failures is a key challenge. In this work, we focus on anomaly detection of data storage systems, and we propose a solution for detecting performance degradation of cloud deployed systems in real time. We use Long Short-term Memory (LSTM) Autoencoders for learning the normal representations and reconstruct the input sequences. Then, we used the reconstructed errors of the LSTM Autoencoders on unseen time series data to detect abnormal behaviours. We used state-of-the-art benchmarks such as TPCx-IoT and YCSB to evaluate the performance of HBase and MongoDB systems. Our experimental analysis shows the ability of the proposed approach to detect abnormal behaviours in cloud systems.

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