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- W2897317087 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." @default.
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- W2897317087 date "2018-07-01" @default.
- W2897317087 modified "2023-09-23" @default.
- W2897317087 title "Association Learning based Hybrid Model for Cloud Workload Prediction" @default.
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- W2897317087 doi "https://doi.org/10.1109/ijcnn.2018.8488996" @default.
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