Association Learning based Hybrid Model for Cloud Workload Prediction

Abstract

Cloud environment provides on-demand access to a shared pool of computing resources over the Internet. Failures are unavoidable in such a distributed and complex environment. In this work, we simulate such a scenario in a docker based virtual environment to aid a proactive approach for anomaly identification in a cloud environment. Proactive approach involves resource prediction first and then anomaly detection. This paper focuses only on resource prediction. We also propose a hybrid model of LSTM and BLSTM using association learning that captures the relationship between the related resource metrics to predict future resource workload in cloud. We use a mix of different types of workloads for simulating the workloads in a cloud environment. The proposed approach is validated on the collected trace of data in a docker based virtual environment as well as the Google cluster trace. It is observed that the proposed model works better as compared to the other state-of-the-art models for resource workload prediction.