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Kumar, Vishwajeet (2025) A Novel Cloud-Native Autoscaling Framework Using MultiView-BiLSTM and Azure Time-Series Data. Masters thesis, Dublin, National College of Ireland.
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Abstract
Autoscaling is a cloud computing technique that dynamically adjusts resource allocation based on real-time workload demands, ensuring optimal performance and cost-efficiency. Traditional autoscaling relies on static, rule-based methods that struggle with unpredictable traffic and often lead to under- or over-provisioning. This study proposes a novel cloud-native autoscaling framework that leverages time-series workload data from Microsoft Azure to forecast resource demand using advanced machine learning (ML) and deep learning (DL) models. The implemented models include K-Nearest Neighbours (KNN), Random Forest (RF), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and a novel MultiView-BiLSTM (MV-BiLSTM) architecture. Among them, the MV-BiLSTM model featuring multiple parallel BiLSTM heads demonstrated the best performance with an Root Mean Squared Error (RMSE) of 0.0194 and R-squared (R²) of 0.9783, making it highly effective for capturing complex temporal patterns. From a cloud deployment perspective, all models were containerised using Docker, pushed to AWS Elastic Container Registry (ECR), and deployed on Elastic Compute Cloud (EC2) through Cloud9, with Simple Storage Service (S3) used for data and result graphs storage.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Cortes Mendoza, Jorge Mario UNSPECIFIED |
| Uncontrolled Keywords: | Autoscaling; Cloud Computing; Machine Learning; Deep Learning; MultiViewBiLSTM |
| Subjects: | T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 26 Mar 2026 15:18 |
| Last Modified: | 26 Mar 2026 15:18 |
| URI: | https://norma.ncirl.ie/id/eprint/9232 |
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