A Supervised Deep Learning Framework for Proactive Anomaly Detection in Cloud Workloads
Shaifu Gupta, Neha Muthiyan, Siddhant Kumar, Aditya Nigam, Dileep Aroor Dinesh · 2017
Cloud environment is highly prone to failures due to its distributed nature and inherent complexity. Proactive identification of failures aids the service providers to avert these failures by taking corrective actions before they actually happen. In this paper, we analyze the resource usage patterns to identify failures due to resource contention in cloud. The resource usage and performance metrics of the working system are analyzed at regular time instants to model the normal and anomalous working behaviors. A two stage framework has been implemented where a hybrid of long short term memory (LSTM) and bidirectional long short term memory (BLSTM) is used to predict the future resource usage and performance metric values in the first stage. In the second stage, the hybrid model is used to classify the expected state as either normal or abnormal. We evaluate the proposed anomaly detection model in a virtual environment set up using Docker containers. The experimental results show that the proposed algorithm outperforms state-of-the-art algorithms.