Source-linked AI summary
Joint Temporal-Structural Representation Learning for Distributed Fault Discrimination in Microservice Architectures
Yihan Xue, Yuxiao Wang, Ao Zhu, Xiaoxuan Sun, Chong Zhang
TL;DR
Microservice fault diagnosis must handle evolving dependencies, heterogeneous observations, and faults that propagate across services. The paper uses temporal graph neural networks to jointly encode aligned multi-source signals, temporal state evolution, and dependency interactions before system-level readout. Comparative evaluation reports superior performance across multiple metrics, supporting the value of joint temporal-structural modeling for distributed fault discrimination.
Problem
Distributed fault diagnosis must distinguish faults amid complex dependencies, time-varying states, and heterogeneous, noisy, incomplete, or misaligned observations.
Method
The model maps aligned multi-source service observations into dynamic graph sequences, applies temporal encoding and attention-based structural message passing, and uses dual node-temporal readout for fault classification.
Results
The proposed method achieves superior results across the reported evaluation metrics under a unified comparison protocol.
Takeaways & Limitations
Joint temporal and dependency-structure modeling provides a unified basis for representing fault-related state transitions and cross-service correlations in microservice systems.
Abstract
from arXiv · showhide
Addressing the diverse fault morphologies, complex dependencies, and time-varying operational states in microservice distributed systems, this paper proposes a distributed fault discrimination model based on temporal graph neural networks. This model characterizes the microservice operation process as a dynamic graph sequence evolving, and performs joint representation learning of temporal modeling and structural interactions within a unified framework. First, service-level multi-source observation signals are aligned and characterized to construct node feature sequences and their corresponding time-dependent dependencies. Then, a temporal coding module is introduced to extract the dynamic evolution representation of service states, and at each time step, attention-based structured message passing is used to characterize dependency interactions and propagation associations, forming a structure-enhanced temporal node representation. Furthermore, a dual readout mechanism is employed to aggregate the node and temporal dimensions, obtaining a system-level global representation and outputting the fault category distribution. Finally, supervised learning objectives are used to optimize model parameters, enabling the model to learn stable discrimination evidence under complex interactions and multi-source noise conditions. Comparative experimental results show that the proposed method achieves superior performance on multiple evaluation metrics, validating the effectiveness of jointly modeling temporal evolution and dependency structures in improving the distributed fault discrimination capability of microservices.
I. INTRODUCTION
Microservice systems combine dynamic dependencies, heterogeneous observations, and faults that propagate across services over time. The paper motivates temporal graph neural networks as a way to jointly represent temporal evolution and structural interactions.
- Motivation: Cloud-native microservices form complex dependency networks whose changing interactions create cross-service propagation and heterogeneous fault patterns.Resource contention, rate limiting, circuit breaking, version rolling, and instance migration contribute to this uncertainty.
- Limitations of existing diagnosis: Single-point metrics and static topologies struggle with concurrent or cascading faults, causing identification and localization errors.
- Core challenge: Distributed fault diagnosis must jointly model temporal evolution, structural dependencies, and noisy, incomplete, misaligned multimodal observations.Metrics, logs, and traces can exhibit delays, amplification, or obscuration along dependent links.
- Core challenge: Temporal-only models miss propagation effects, whereas structure-only models miss state transitions and temporal characteristics around faults.
- Proposed direction: Temporal graph neural networks jointly learn evolving node states and service-dependency interactions on dynamic graphs.They can combine short-term mutations, long-term drift, neighborhood dependencies, and cross-layer propagation information.
II. METHODOLOGY FOUNDATION
The methodological foundation combines graph-based dependency modeling, sequential temporal learning, robust learning under data variation, and multi-source representation fusion. Together, these strands motivate dynamic graph sequences with dependency-aware message passing and system-level aggregation.
- Graph-based dependency modeling: Graph neural networks encode inter-service call and traffic dependencies, but static or weakly dynamic structures limit modeling of evolving system states.
- Graph-based dependency modeling: Spatiotemporal representation learning combines graph structures with sequence modeling to capture structural correlations and temporal transitions.This provides a basis for dynamic graph sequences whose node states evolve while dependency-aware message passing is preserved.
- Sequential temporal learning: Sequential learning emphasizes aligning heterogeneous time-series observations and modeling long-term dependencies and rare fault patterns.
- Robust learning: Self-supervised and meta-learning strategies address class imbalance, distribution shift, and few-shot or rare-event diagnosis settings.
- Multi-source integration: Shared representation learning and anomaly-detection methods support fusion of metrics, logs, traces, and multi-scale temporal signals.
- System-level representation: Memory-driven, multi-agent, and language-model approaches motivate long-horizon aggregation, distributed coordination, and semantic reasoning over system states.
III. METHOD
The method represents aligned service observations as a time-evolving directed graph, encodes temporal states, and performs attention-weighted structural message passing. Dual readout then produces a system representation for supervised fault classification.
- Dynamic graph construction: After multi-source signals are aligned to service granularity, each time step is represented by service nodes and time-dependent invocation edges.The service-node set remains fixed within the time window, while the edge set changes over time.
- Dynamic graph construction: Directed edges follow caller-to-callee relationships so information aggregation aligns with service invocation and fault-propagation paths.
- Temporal encoding: A shared temporal encoder uses gated recurrent updates to jointly represent short-term perturbations and long-term drift in service states.The resulting temporal context conditions later structural interactions.
- Structural interaction modeling: At each time step, attention adaptively weights dependency strengths before aggregating neighborhood messages into structural representations.
- Structural interaction modeling: Temporal hidden states and structural messages are fused into structure-enhanced node representations under temporal context constraints.
- System-level readout: Dual readout pools node representations at each time step and then aggregates across time to form a global system representation.A classifier maps this representation to fault-category probabilities.
- System-level readout: A supervised objective trains discriminative system-level representations, with mean or attention pooling supporting systems of different scales.
A. Dataset
The study uses GAIA, an open-source microservice fault-diagnosis benchmark built from the MicroSS simulation system. It combines multi-source observability data with controlled fault injections and aligned labels.
- Dataset composition: GAIA integrates metrics, logs, traces, runtime observations, and controlled fault-injection records for microservice diagnosis.
- Data organization: The MicroSS subset organizes metric, trace, log, and runtime records into separate data directories.
- Data organization: Trace data includes timestamps, service identifiers, trace and span IDs, parent–child relationships, and request status information for constructing time-varying interactions.
- Label alignment: Runtime logs and anomaly-injection records support alignment between system-level fault labels and service-level observations.
B. Experimental Results and Analysis
The evaluation compares representative microservice anomaly-detection and root-cause-localization methods across classification, detection, and consistency metrics. The proposed method achieves superior results across these metrics, with improved consistency supporting more reliable fault handling and system-level decisions.
- Evaluation setup: The evaluation compares representative methods under a unified protocol using classification, detection, and consistency metrics.These metrics characterize complementary dimensions of distributed fault discrimination.
- Comparative results: The proposed method achieves superior results across all reported metrics, indicating stronger stability and generalization.The analysis links the gains to balanced precision and recall across failure classes.
- Comparative results: Improved consistency produces more focused outputs with fewer error types, supporting fault handling and system-level decision-making.
- Propagation analysis: The dynamic call graph exposes dense coupling and multi-hop propagation paths, highlighting the need to model local and long-range dependencies with temporal context.Gray edges summarize service calls, while red nodes and arrows identify fault impacts and propagation paths.
V. CONCLUSION
The paper proposes temporal graph neural modeling that unifies service dependency structure, runtime evolution, and system-level representations for distributed fault identification. It also describes production-oriented uses and broader extensions toward trustworthy operation and stability governance.
- Methodological contribution: The proposed approach maps multi-source observations into dynamic graph sequences and jointly models temporal encoding, structural interaction, and system-level readout.
- Application impact: By incorporating service dependencies and call interactions, the framework supports fault identification and decision-making for complex operational scenarios.The stated operations include alarm aggregation, impact-scope identification, and handling-priority ranking.
- Broader significance: Sequence graph modeling provides a unified representation framework for connecting call topology, operational signals, and failure semantics.The paper identifies possible extensions to system health assessment, risk prediction, dependency optimization, and elastic control.