政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152035

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题名:	基於數據驅動與模組化篩選機制的自適應HPA模組設計與實現 Design and Implementation of an Adaptive HPA Module Based on Data-Driven and Modular Filtering Mechanisms
作者:	王盛泰 Wang, Sheng-Tai
贡献者:	張宏慶 Jang, Hung-Chin 王盛泰 Wang, Sheng-Tai
关键词:	水平自動縮放微服務架構數據驅動雲端計算容器資源管理穩定性機器學習服務品質 Horizontal Pod Autoscaler Microservice architecture Data-driven Cloud computing Containers Resource management stability Machine learning Quality of service
日期:	2024
上传时间:	2024-07-01 12:30:14 (UTC+8)
摘要:	微服務是近年來，很受歡迎的應用程式部署方式，透過對應用程式解偶，使其成為功能獨立的微服務，就可以讓應用程式某些遇上需求瓶頸的微服務水平擴充，而不影響到其它微服務。透過利用Kubernetes平台與其相關的網路套件，我們可以自動化將服務請求導向至對應的微服務實體，並且依照特定規則與閾值進行自動化水平擴展(Horizontal Pod Autoscaler, HPA)。現有的HPA模組透過設定同服務容器的平均資源使用率與監控當前資源使用率，動態調整提供服務所需的容器數量。現有的HPA模組的演算法運算簡單並且實作容易，運作起來也有一定的績效，目前已被kubernetes平台使用多年。它能確保資源使用率，但它無法直接針對服務品質進行有系統的分析與並根據結果提出縮放決策。除此之外，當需求流量變化很大時，縮放週期短會導致頻繁新建或終止多個容器，造成系統不穩定與產生額外的系統負擔；相反地在週期長時，則會跟不上流量的變化，導致服務品質不穩定，甚至極端狀況可能會劇烈增加到無法接受的程度。本研究提出一種模組化的篩選方法，並依照此流程實作可以確保服務品質並維持穩定性的HPA模組。此模組又分為三個部分，第一部分透過預測模組確保可能的決策行為是否合乎服務品質；第二部分透過篩選模組，依照所需的策略遴選出最好的決策；第三部分透過穩定性模組，從最佳決策與穩定性決策中選出最終決策。研究結果顯示，提出的HPA模組在實驗的所有情況下，依然保持與現有算法接近的平均CPU使用率與平均反應時間，但大幅減少了運行期間所新建或刪除的容器數。最後本研究提出還可以進一步改善的部分，以及可以考量的實驗設計，以供未來從事相關研究的人參考。 Microservices have become a popular application deployment method, allowing inde-pendent scaling of specific microservices without affecting others. Using the Kubernetes platform and related network packages, service requests can be automated to route to corre-sponding microservices and perform horizontal scaling. One specific implementation of horizontal scaling is the Horizontal Pod Autoscaler (HPA), which adjusts the number of service containers dynamically by setting and monitoring average resource usage. The ex-isting HPA module uses a simple algorithm that has been easy to implement and effective for years on Kubernetes. While it ensures resource utilization, it cannot systematically ana-lyze service quality or make scaling decisions based on these analyses. Frequent scaling due to demand fluctuations can cause system instability and extra burden, while slow scaling can't keep up with traffic changes, affecting service quality. This study proposes a modular filtering method to ensure service quality and stability in HPA, divided into three parts: a prediction module to ensure decisions comply with service quality, a filtering module to select the best strategy, and a stability module to choose the final decision. The proposed HPA module maintains CPU utilization and response times close to the existing algorithm but significantly reduces container creation and deletion during operation. Lastly, the study suggests areas for improvement and experimental design considerations for future research.
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Zheng, "Microscaler: Cost-Effective Scaling for Microservice Applications in the Cloud With an Online Learning Approach," in IEEE Transactions on Cloud Computing, vol. 10, no. 2, pp. 1100-1116, 1 April-June 2022, doi: 10.1109/TCC.2020.2985352. [17] S. Roy, A. C. König, I. Dvorkin and M. Kumar, "PerfAugur: Robust diagnostics for per-formance anomalies in cloud services," 2015 IEEE 31st International Conference on Data Engineering, Seoul, Korea (South), 2015, pp. 1167-1178, doi: 10.1109/ICDE.2015.7113365. [18] Z. Xiao and S. Hu, "DScaler: A Horizontal Autoscaler of Microservice Based on Deep Reinforcement Learning," 2022 23rd Asia-Pacific Network Operations and Manage-ment Symposium (APNOMS), Takamatsu, Japan, 2022, pp. 1-6, doi: 10.23919/APNOMS56106.2022.9919994. [19] Horn, A., Fard, H.M., Wolf, F. (2022). Multi-objective Hybrid Autoscaling of Micro-services in Kubernetes Clusters. In: Cano, J., Trinder, P. (eds) Euro-Par 2022: Parallel Processing. Euro-Par 2022. 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A survey of Kubernetes scheduling algorithms. J Cloud Comp 12, 87 (2023). https://doi.org/10.1186/s13677-023-00471-1 [25] Kubernetes.io, "Horizontal Pod Autoscaler," Kubernetes Documentation. https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/.
描述:	碩士國立政治大學資訊科學系 110753147
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0110753147
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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