Multimodal cloud resources utilization forecasting using a Bidirectional Gated Recurrent Unit predictor based on a power efficient Stacked denoising Autoencoders

To reap the advantages of many continual growing cloud services, cloud industries should adopt smart and holistic resources scheduling strategies. By deploying efficient deep learning technologies, many chaotic cloud traffics' potential issues may be solved. Toward efficient cloud instances rightsizing and scheduling, we adopt in this paper a new Bidirectional Gated Recurrent Unit predictor based on a power efficient Stacked Denoising Autoencoders to forecast simultaneously future hourly virtual CPU, memory, and storage utilizations. Using various data ranges of resources under three AWS instances families, the best forecasting results achieved so far [1,83,30,78,331,11] of mean RMSE values and [1,37,21,63,245,13] of mean MAE values while predicting respectively future vCPU, memory, and storage utilizations. In addition, the proposed model also proved its precision stability and outperformance over the three considered SDAEGRU, SDAE-LSTM and BiGRU benchmark models. Given the neglected power consumption measurement noticed in most related studies, we eventually validated the proposed predictor's power efficiency by measuring in addition its real time consumed power in watt and temperature throughout the training process duration. The proposed predictor decreased the average consumed power by 5% compared to a classical sparse AE-BiGRU. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

This paper investigates the adoption of smart and holistic resource scheduling strategies in cloud industries to leverage the benefits of rapidly growing cloud services. By deploying efficient deep learning technologies, the potential issues related to chaotic cloud traffic can be addressed. The paper proposes a new Bidirectional Gated Recurrent Unit (BiGRU) predictor based on a power-efficient Stacked Denoising Autoencoders (SDAE) to forecast future virtual CPU, memory, and storage utilizations. Experimental results demonstrate that the proposed predictor achieves the best forecasting results compared to other benchmark models, proving its precision stability and outperformance. Moreover, the paper validates the proposed predictor's power efficiency by measuring its real-time power consumption and temperature during the training process. The proposed predictor reduces the average consumed power by 5% compared to a classical sparse AE-BiGRU.