  Microservice-based systems (MSS) may experience failures in various fault
categories due to their complex and dynamic nature. To effectively handle
failures, AIOps tools utilize trace-based anomaly detection and root cause
analysis. In this paper, we propose a novel framework for few-shot abnormal
trace classification for MSS. Our framework comprises two main components: (1)
Multi-Head Attention Autoencoder for constructing system-specific trace
representations, which enables (2) Transformer Encoder-based Model-Agnostic
Meta-Learning to perform effective and efficient few-shot learning for abnormal
trace classification. The proposed framework is evaluated on two representative
MSS, Trainticket and OnlineBoutique, with open datasets. The results show that
our framework can adapt the learned knowledge to classify new, unseen abnormal
traces of novel fault categories both within the same system it was initially
trained on and even in the different MSS. Within the same MSS, our framework
achieves an average accuracy of 93.26\% and 85.2\% across 50 meta-testing tasks
for Trainticket and OnlineBoutique, respectively, when provided with 10
instances for each task. In a cross-system context, our framework gets an
average accuracy of 92.19\% and 84.77\% for the same meta-testing tasks of the
respective system, also with 10 instances provided for each task. Our work
demonstrates the applicability of achieving few-shot abnormal trace
classification for MSS and shows how it can enable cross-system adaptability.
This opens an avenue for building more generalized AIOps tools that require
less system-specific data labeling for anomaly detection and root cause
analysis.
