Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model
The resource usage behaviors of application workloads are currently the primary concern of cloud providers offering hosting services. These services should be able to adapt to workload changes by automatically provisioning and de-provisioning resources so that, at all times, the existing resources i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10066275/ |
| _version_ | 1797866991004942336 |
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| author | Mohamed Samir Khaled T. Wassif Soha H. Makady |
| author_facet | Mohamed Samir Khaled T. Wassif Soha H. Makady |
| author_sort | Mohamed Samir |
| collection | DOAJ |
| description | The resource usage behaviors of application workloads are currently the primary concern of cloud providers offering hosting services. These services should be able to adapt to workload changes by automatically provisioning and de-provisioning resources so that, at all times, the existing resources in a system match the current service demand. Such behavior can be achieved manually by hiring a DevOps team to manage the application’s resources. Another option would be automating the resource provisioning processing using automated rules. Once such rules are met, the hosting environment will scale the resources accordingly. However, managing a DevOps team or creating flaky rules can lead to over-scaling application resources. This work proposes a new approach: a proactive auto-scaling framework built on an ensemble model. Such a model utilizes several machine learning techniques to scale application resources to match resource demand before the need arises. We evaluated our solution against three real production applications hosted on Cegedim Cloud Hosting Environment, an industrial environment serving several cloud applications from various domains, and against other machine learning models used in similar proactive auto-scaling experiments mentioned in past work. The experimentation results show that predicting application resources like CPU or RAM is feasible. Moreover, even in production environments, our ensemble model performs optimally in the CPU case and is near the optimal model when predicting RAM resources. |
| first_indexed | 2024-04-09T23:33:09Z |
| format | Article |
| id | doaj.art-97e80a81edee41dab608247ff22f41b5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T23:33:09Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-97e80a81edee41dab608247ff22f41b52023-03-20T23:00:33ZengIEEEIEEE Access2169-35362023-01-0111250082501910.1109/ACCESS.2023.325630210066275Proactive Auto-Scaling Approach of Production Applications Using an Ensemble ModelMohamed Samir0https://orcid.org/0000-0003-3192-3337Khaled T. Wassif1https://orcid.org/0000-0002-7401-5219Soha H. Makady2https://orcid.org/0000-0002-3330-6204Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptFaculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptFaculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptThe resource usage behaviors of application workloads are currently the primary concern of cloud providers offering hosting services. These services should be able to adapt to workload changes by automatically provisioning and de-provisioning resources so that, at all times, the existing resources in a system match the current service demand. Such behavior can be achieved manually by hiring a DevOps team to manage the application’s resources. Another option would be automating the resource provisioning processing using automated rules. Once such rules are met, the hosting environment will scale the resources accordingly. However, managing a DevOps team or creating flaky rules can lead to over-scaling application resources. This work proposes a new approach: a proactive auto-scaling framework built on an ensemble model. Such a model utilizes several machine learning techniques to scale application resources to match resource demand before the need arises. We evaluated our solution against three real production applications hosted on Cegedim Cloud Hosting Environment, an industrial environment serving several cloud applications from various domains, and against other machine learning models used in similar proactive auto-scaling experiments mentioned in past work. The experimentation results show that predicting application resources like CPU or RAM is feasible. Moreover, even in production environments, our ensemble model performs optimally in the CPU case and is near the optimal model when predicting RAM resources.https://ieeexplore.ieee.org/document/10066275/Auto-scalingresource allocationdynamic resource provisioningresource management on clouds |
| spellingShingle | Mohamed Samir Khaled T. Wassif Soha H. Makady Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model IEEE Access Auto-scaling resource allocation dynamic resource provisioning resource management on clouds |
| title | Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model |
| title_full | Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model |
| title_fullStr | Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model |
| title_full_unstemmed | Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model |
| title_short | Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model |
| title_sort | proactive auto scaling approach of production applications using an ensemble model |
| topic | Auto-scaling resource allocation dynamic resource provisioning resource management on clouds |
| url | https://ieeexplore.ieee.org/document/10066275/ |
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