QQ客服 官方微博 反馈留言

	Modeling Application Performance in Docker Containers using Machine Learning Techniques
	Kejiang Ye; Yanmin Kou; Chengzhi Lu; Yang Wang; Cheng-Zhong Xu
	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
内容类型	会议论文
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.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。