Modeling Application Performance in Docker Containers using Machine Learning Techniques
文献类型：会议论文
| 作者 | Kejiang Ye; Yanmin Kou; Chengzhi Lu; Yang Wang; Cheng-Zhong Xu |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 新加坡 |
| 英文摘要 | Docker container is experiencing a rapid development with the support from industry like Google and is being widely used in large scale production cloud environments. However the performance of applications running in Docker containers is still not clear due to the complex relationship between container resource allocation and application performance. In this paper, we first study the impact of key parameters in container resource allocation that affect the performance of containerized applications. Then, we present modeling techniques over CPU, memory and I/O resources to characterize the performance of applications running in containers. To address this multi-dimensional modeling problem, we propose three machine learning techniques, i.e. Linear Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN). We implement and evaluate the modeling techniques for four complex benchmark workloads from Spark. Experimental results demonstrate the proposed models can achieve as low as 2.27\% prediction error, with an average of 10.13\% for most applications. Furthermore, the prediction accuracy of SVM and ANN models are substantially better than LR based approaches, with 48.13\% and 29.30\% improvement. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14121]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Kejiang Ye,Yanmin Kou,Chengzhi Lu,et al. Modeling Application Performance in Docker Containers using Machine Learning Techniques[C]. 见:. 新加坡. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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