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

Alexandria Engineering Journal

Medromi Hicham¹  Courboulay Vincent²  Duthil Benjamin³  Hamzaoui Ikhlasse⁴ 

[1] Corresponding author.;EIGSI, la Rochelle, France;System Architecture Team (EAS), Engineering Research Laboratory (LRI), National High School of Electricity and Mechanic (ENSEM), Hassan II University, 8118 Casablanca, Morocco;Research Foundation for Development and Innovation in Science and Engineering, 16469 Casablanca, Morocco;

关键词: Multimodal instances prediction;    Stacked Denoising Auto Encoders;    Bidirectional Gated Recurrent Unit;    Time series;    Power efficiency;   

DOI  :  

来源: DOAJ

【 摘 要 】

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 SDAE-GRU, 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.

【 授权许可】

Unknown