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Dhumal, Shreya (2023) Improving Dynamic Cloud Workload Prediction and Resource Management with AdaptiveCloudEnsemble(ACE): A Concept Drift-aware Approach. Masters thesis, Dublin, National College of Ireland.
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Abstract
In the ever-changing landscape of cloud computing, dynamic resource allocation is a crucial challenge, particularly in handling fluctuating workloads and navigating the intricacies of concept drift. Traditional machine learning-based resource allocation methods, while promising, frequently fail when confronted with the dynamic and unexpected nature of real-world cloud workloads. Although ensemble learning has emerged as a potential answer, many existing models are still struggling with real-time adaptation and effective drift management. This research offers the AdaptiveCloudEnsemble (ACE) technique, a new solution for stream management and development of models done using AWS Cloud resources. Within a unified ensemble architecture, the ACE approach integrates many predictive algorithms and advanced drift detection systems. By integrating AWS services for data streaming and analysis, the ACE model not only improves adaptability and accuracy in resource management but also establishes a new benchmark in leveraging cloud infrastructures for solving complex machine learning problems.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Arun, Shreyas Setlur UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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 2025 16:55 |
| Last Modified: | 26 Mar 2025 16:55 |
| URI: | https://norma.ncirl.ie/id/eprint/7340 |
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