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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.08308 |
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| _version_ | 1866915236867473408 |
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| author | Xie, Shuaiyu Wang, Jian Luo, Yang Yong, Yunqing Tan, Yuzhen Li, Bing |
| author_facet | Xie, Shuaiyu Wang, Jian Luo, Yang Yong, Yunqing Tan, Yuzhen Li, Bing |
| contents | Auto-scaling is an automated approach that dynamically provisions resources for microservices to accommodate fluctuating workloads. Despite the introduction of many sophisticated auto-scaling algorithms, evaluating auto-scalers remains time-consuming and labor-intensive, as it requires the implementation of numerous fundamental interfaces, complex manual operations, and in-depth domain knowledge. Besides, frequent human intervention can inevitably introduce operational errors, leading to inconsistencies in the evaluation of different auto-scalers. To address these issues, we present ScalerEval, an end-to-end automated and consistent testbed for auto-scalers in microservices. ScalerEval integrates essential fundamental interfaces for implementation of auto-scalers and further orchestrates a one-click evaluation workflow for researchers. The source code is publicly available at \href{https://github.com/WHU-AISE/ScalerEval}{https://github.com/WHU-AISE/ScalerEval}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08308 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ScalerEval: Automated and Consistent Evaluation Testbed for Auto-scalers in Microservices Xie, Shuaiyu Wang, Jian Luo, Yang Yong, Yunqing Tan, Yuzhen Li, Bing Software Engineering Auto-scaling is an automated approach that dynamically provisions resources for microservices to accommodate fluctuating workloads. Despite the introduction of many sophisticated auto-scaling algorithms, evaluating auto-scalers remains time-consuming and labor-intensive, as it requires the implementation of numerous fundamental interfaces, complex manual operations, and in-depth domain knowledge. Besides, frequent human intervention can inevitably introduce operational errors, leading to inconsistencies in the evaluation of different auto-scalers. To address these issues, we present ScalerEval, an end-to-end automated and consistent testbed for auto-scalers in microservices. ScalerEval integrates essential fundamental interfaces for implementation of auto-scalers and further orchestrates a one-click evaluation workflow for researchers. The source code is publicly available at \href{https://github.com/WHU-AISE/ScalerEval}{https://github.com/WHU-AISE/ScalerEval}. |
| title | ScalerEval: Automated and Consistent Evaluation Testbed for Auto-scalers in Microservices |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2504.08308 |