<abstract xmlns="http://www.w3.org/1999/xhtml"><p>For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.</p></abstract>

For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for...

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Online veröffentlicht: 10. Dez. 2020

Seitenbereich: 55 - 73

Eingereicht: 28. Sept. 2020

Akzeptiert: 29. Okt. 2020

DOI: https://doi.org/10.2478/cait-2020-0047

Schlüsselwörter
Cloud computing, Improved LSTM neural network, Multilayer perceptron network, Opposition-based differential evolution, Workload prediction

© 2020 K. Dinesh Kumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Online veröffentlicht: 10. Dez. 2020

Seitenbereich: 55 - 73

Eingereicht: 28. Sept. 2020

Akzeptiert: 29. Okt. 2020

DOI: https://doi.org/10.2478/cait-2020-0047

SchlüsselwörterCloud computing, Improved LSTM neural network, Multilayer perceptron network, Opposition-based differential evolution, Workload prediction

© 2020 K. Dinesh Kumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Cloud computing, Improved LSTM neural network, Multilayer perceptron network, Opposition-based differential evolution, Workload prediction