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	Fault Injection and Detection for Artificial Intelligence Applications in Container-based Clouds
	Kejiang Ye; Yangyang Liu; Guoyao Xu; Cheng-Zhong Xu
	2018
会议日期	2018
会议地点	美国西雅图
英文摘要	Container technique is increasingly used to build modern cloud computing systems to achieve higher efficiency and lower resource costs, as compared with traditional virtual machine technique. Artificial intelligence (AI) is a mainstream method to deal with big data, and is used in many areas to achieve better effectiveness. It is known that attacks happen every day in production cloud systems, however, the fault behaviors and interferences of up-to-date AI applications in container-based cloud systems is still not clear. This paper aims to study the reliability issue of container-based clouds. We first propose a fault injection framework for container-based cloud systems. We build a docker container environment installed with TensorFlow deep learning framework, and develop four typical attack programs, i.e., CPU attack, Memory attack, Disk attack and DDOS attack. Then, we inject the attack programs to the containers running AI applications (CNN, RNN, BRNN and DRNN), to observe fault behaviors and interferences phenomenon. After that, we design fault detection models based on quantile regression method to detect potential faults in containers. Experimental results show the proposed fault detection models can effectively detect the injected faults with more than 60% Precision, more than 90% Recall and nearly 100% Accuracy.
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/172644/14111]
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,Yangyang Liu,Guoyao Xu,et al. Fault Injection and Detection for Artificial Intelligence Applications in Container-based Clouds[C]. 见:. 美国西雅图. 2018.

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