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.
Compte des citations dans Scopus : 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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