From distributed tracing to proactive SLO management: a mini-review of trace-driven performance prediction for cloud-native microservices
Year: 2026
Type: review
Abstract: Cloud-native microservices improve development velocity and elasticity, but they also create complex and dynamic service dependencies. Resource contention, queue buildup, and downstream slowdowns can propagate through call chains, amplifying end-to-end tail latency (e.g., p95/p99) and increasing Service Level Objective (SLO) violation risks. While many studies focus on post-hoc anomaly detection and root-cause analysis, industrial operations increasingly demand proactive capabilities, like predicting performance risks before a request finishes, issuing early warnings from partial trace prefixes, and producing actionable signals for mitigation. This mini-review synthesizes recent progress on trace-driven proactive SLO management. We summarize problem formulations and evaluation protocols for SLO violation and tail-quantile prediction, prefix early warning under precision constraints, and actionable intermediate outputs such as bottleneck candidate ranking and what-if estimation. We then survey modeling approaches spanning feature-based baselines, sequence models, graph neural networks, sequence-graph fusion, and multimodal/causal extensions, highlighting practical issues such as class imbalance, sampling-induced missing spans, and topology drift. Finally, we survey commonly used public benchmarks and traces, and discuss open challenges toward deployable, trustworthy proactive SLO management.
Source: Frontiers in Computer Science
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,418
OpenAlex ID: W7130384521