Automatic data featurization for enhanced proactive service auto-scaling: Boosting forecasting accuracy and mitigating oscillation

Bali, Ahmed, Houm, Yassine El, Gherbi, Abdelouahed et Cheriet, Mohamed. 2024. « Automatic data featurization for enhanced proactive service auto-scaling: Boosting forecasting accuracy and mitigating oscillation ». Journal of King Saud University - Computer and Information Sciences, vol. 36, nº 2.

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Résumé

Edge computing has gained widespread adoption for time-sensitive applications by offloading a portion of IoT system workloads from the cloud to edge nodes. However, the limited resources of IoT edge devices hinder service deployment, making auto-scaling crucial for improving resource utilization in response to dynamic workloads. Recent solutions aim to make auto-scaling proactive by predicting future workloads and overcoming the limitations of reactive approaches. These proactive solutions often rely on time-series data analysis and machine learning techniques, especially Long Short-Term Memory (LSTM), thanks to its accuracy and prediction speed. However, existing auto-scaling solutions often suffer from oscillation issues, even when using a cooling-down strategy. Consequently, the efficiency of proactive auto-scaling depends on the prediction model accuracy and the degree of oscillation in the scaling actions. This paper proposes a novel approach to improve prediction accuracy and deal with oscillation issues. Our approach involves an automatic featurization phase that extracts features from time-series workload data, improving the prediction’s accuracy. These extracted features also serve as a grid for controlling oscillation in generated scaling actions. Our experimental results demonstrate the effectiveness of our approach in improving prediction accuracy, mitigating oscillation phenomena, and enhancing the overall auto-scaling performance.

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Gherbi, Abdelouahed Cheriet, Mohamed
Affiliation:	Génie logiciel et des technologies de l'information, Génie des systèmes
Date de dépôt:	14 févr. 2024 19:05
Dernière modification:	11 mars 2024 15:51
URI:	https://espace2.etsmtl.ca/id/eprint/28349

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