A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency Scenarios
Cloud computing technology is widely embraced due to its ability to meet diverse computing resource demands from users. As the user base grows, the challenge for cloud service providers to dynamically allocate resources to applications according to real-time customer needs becomes more daunting. Exi...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10535107/ |
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| author | Haipeng Liu Wenhao Zhu Siyi Fu Yongjun Lu |
| author_facet | Haipeng Liu Wenhao Zhu Siyi Fu Yongjun Lu |
| author_sort | Haipeng Liu |
| collection | DOAJ |
| description | Cloud computing technology is widely embraced due to its ability to meet diverse computing resource demands from users. As the user base grows, the challenge for cloud service providers to dynamically allocate resources to applications according to real-time customer needs becomes more daunting. Existing auto-scaling mechanisms, such as Horizontal Pod Autoscaler (HPA), lack flexibility in handling transient and irregular fluctuations in resource demands, making it difficult to respond quickly. This research focuses on auto-scaling solutions for applications in high-concurrency scenarios. The study enhances the MAPE structure of traditional auto-scaling models and introduces a trend detection module in a proactive auto-scaling engine based on predictive algorithms. This module detects trends during transient irregular request volume fluctuations, corrects prediction algorithm results inconsistent with the current trend, and provides more reasonable resource allocation for programs. Experimental results, using both real access peak data and simulated data in a Kubernetes environment, indicate that the proposed auto-scaler effectively prevents resource shortages in high-concurrency network environments compared to other auto-scaling mechanisms. It ensures the performance and availability of applications while reducing resource wastage. |
| format | Article |
| id | doaj-art-871f1f0143d94ad5beb3d07e2119e70e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-871f1f0143d94ad5beb3d07e2119e70e2024-12-19T00:00:30ZengIEEEIEEE Access2169-35362024-01-0112718217183410.1109/ACCESS.2024.340345110535107A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency ScenariosHaipeng Liu0https://orcid.org/0009-0002-5831-8284Wenhao Zhu1https://orcid.org/0000-0002-9656-9781Siyi Fu2https://orcid.org/0009-0003-5008-9330Yongjun Lu3School of Computer Science, Shanghai University, Shanghai, ChinaSchool of Computer Science, Shanghai University, Shanghai, ChinaSchool of Computer Science, Shanghai University, Shanghai, ChinaShanghai Center for Student Affairs, Shanghai, ChinaCloud computing technology is widely embraced due to its ability to meet diverse computing resource demands from users. As the user base grows, the challenge for cloud service providers to dynamically allocate resources to applications according to real-time customer needs becomes more daunting. Existing auto-scaling mechanisms, such as Horizontal Pod Autoscaler (HPA), lack flexibility in handling transient and irregular fluctuations in resource demands, making it difficult to respond quickly. This research focuses on auto-scaling solutions for applications in high-concurrency scenarios. The study enhances the MAPE structure of traditional auto-scaling models and introduces a trend detection module in a proactive auto-scaling engine based on predictive algorithms. This module detects trends during transient irregular request volume fluctuations, corrects prediction algorithm results inconsistent with the current trend, and provides more reasonable resource allocation for programs. Experimental results, using both real access peak data and simulated data in a Kubernetes environment, indicate that the proposed auto-scaler effectively prevents resource shortages in high-concurrency network environments compared to other auto-scaling mechanisms. It ensures the performance and availability of applications while reducing resource wastage.https://ieeexplore.ieee.org/document/10535107/Cloud computingauto-scalerpredictionproactive controllerhigh-concurrencytrend detection |
| spellingShingle | Haipeng Liu Wenhao Zhu Siyi Fu Yongjun Lu A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency Scenarios IEEE Access Cloud computing auto-scaler prediction proactive controller high-concurrency trend detection |
| title | A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency Scenarios |
| title_full | A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency Scenarios |
| title_fullStr | A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency Scenarios |
| title_full_unstemmed | A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency Scenarios |
| title_short | A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency Scenarios |
| title_sort | trend detection based auto scaling method for containers in high concurrency scenarios |
| topic | Cloud computing auto-scaler prediction proactive controller high-concurrency trend detection |
| url | https://ieeexplore.ieee.org/document/10535107/ |
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