An Elaborate Container Cascading Fault Detection Strategy Based on Spatial-Temporal Correlation Model and Co-Optimization

Abstract

Containers are light, numerous, and interdependent, which are prone to cascading fault, increasing the probability of fault and the difficulty of detection. Fault detection methods used in traditional cloud platforms will result in degraded detection performance when applied to the containerized cloud platform. This paper argues that there are usually two reasons that constrain the effectiveness of current cloud fault detection: (1) fault type level: the complexity of the cloud leads to a diversity of fault, many fault types are difficult to detect, such as container cascading fault; (2) fault data level: the imbalance of fault number data to the detection method to bring interference. Therefore, this paper proposes a Container cascading fault Detection strategy based on Spatial-temporal correlation model and Collaborative optimization(CDSC), by extracting the temporal and spatial correlation semantics between faulty containers, constructing the container cascade correlation model, and using dynamic feedback sampling combined with multi-model cooperative optimization training to improve the detection effect of container cascading fault. It is proved that this method improves the detection precision, recall rate and F1 value by 10%-15% on average compared with existing methods.