Abstract
The increasing usage of remote services and high marketplace competition requires cloud service providers to plan and provision computing resources efficiently, while providing affordable services and managing their data center expenditures. Generally, IaaS cloud resources are managed by predicting either long-term workload or long-term resource utilization pattern. But it does not give any genuine information about the necessary memory/CPU before exposing it to the physical machine. So, the prediction of dynamic virtual machine (VM) provisioning is a challenging problem in cloud computing. In this paper, we explored CPU usage details of VMs in Azure cloud dataset to predict utilization patterns. The dataset is used to train several deep learning models. Training with CPU utilization as the target class, we predict the minimum, maximum, and average CPU utilization values. The results are then analyzed using multiple evaluation metrics. After evaluating the different models, we conclude that the GRU performs better in predicting the CPU utilization.
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Padhi, B., Reza, M., Gupta, I., Nagendra, P.S., Kumar, S.S. (2022). Prediction of Dynamic Virtual Machine (VM) Provisioning in Cloud Computing Using Deep Learning. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_46
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DOI: https://doi.org/10.1007/978-981-16-9447-9_46
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