FullSight: A Feasible Intelligent and Collaborative Framework for Service Function Chains Failure Detection

Abstract

Network function virtualization (NFV) is a ground-breaking technology that decouples network functions (NFs) from customized hardware to support more flexible network services and network resource allocation. However, these improvements also lead to an increase in the possibility of service function chain (SFC) failure due to hardware failures, software bugs, or resource contention. This could lead to minor problems or even serious consequences. Unfortunately, the existing failure detection methods have multiple issues, such as small detection range, single detection function, heavy overhead, and low accuracy. Consequently, we propose FullSight, a feasible framework based on deep learning (DL) models that can efficiently integrate both the control plane and programmable data plane for fault detection and classification. This framework obtains the status and indicators of components and network that cause service quality performance degradation through two planes. These indicators are ultimately sent to the knowledge plane for preprocessing, dimensionality reduction, and fault analysis. In addition, we propose two algorithms based on text convolutional neural network (textCNN) and bidirectional encoder representations from transformers (BERT) to classify SFC faults. We implement and evaluate the proposed FullSight prototype extensively on a prototype with thirteen programmable switches and twenty end-hosts. Our experimental results show that FullSight can rapidly and accurately detect and identify eight categories of fine-grained SFC failures, compared with other state-of-the-art methods. Besides, compared with SFC Path Tracer and Pingmesh, our framework can reduce the average bandwidth overhead of the data plane by 57% and 84%, respectively, and achieve detection accuracy of more than 98%.