Fault Injection and Detection for Artificial Intelligence Applications in Container-Based Clouds

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.