- 期刊
- OpenAccess
Adaptive Learning based Prediction Framework for Cloud Datacenter Networks' Workload Anticipation
摘要
Cloud computing has effectively changed the computing industry by introducing the on-demand resources through virtualization. However, a cloud system suffers with several challenges including low resource utilization, high power consumption, security and many others. This paper introduces a neural network based workload forecasting model using differential evolution. The predictive framework is evaluated on five real world data traces. The forecast efficacy is compared with state-of-art approaches including back propagation and linear regression along with statistical analysis. It was observed that the proposed scheme reduced the forecast error up to 85.52% and 89.70% measured using RMSE and MAE respectively. The statistical analysis also validates the superiority of the proposed predictive framework as it received the best rank in the Friedman test analysis.
延伸閱讀
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