Abstract
Exponential growth in the use of cloud computing services makes it difficult to forecast loads of virtual machines (VMs). Accurate virtual machine (VM) workload forecasting is the most critical task in appropriately managing cloud resources such as memory and central processing units while minimizing energy usage. To address this problem, an integrated deep learning model is proposed in this research paper. The model employs two popular neural networks: a bidirectional Long Short-Term Memory Network (BiLSTM) with a convolutional neural networks (CNN). The CNN component pulls high-level attributes from all VM workload data, whereas the BiLSTM component forecasts future VM workload. The experimental results reveal that the suggested model outperforms commonly used workload prediction methods in terms of forecasting accuracy of VMs workloads in cloud computing environments.
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Leka, H.L., Fengli, Z., Kenea, A.T., Sharma, D.P., Tegene, A.T. (2023). Workload Prediction of Virtual Machines Using Integrated Deep Learning Approaches Over Cloud Data Centers. In: Bhattacharyya, S., Banerjee, J.S., Köppen, M. (eds) Human-Centric Smart Computing. Smart Innovation, Systems and Technologies, vol 316. Springer, Singapore. https://doi.org/10.1007/978-981-19-5403-0_5
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