KLD: Kinetic Log-Aware Joint Learning for Dynamic Microservice Diagnosis

Dynamic microservice diagnosis enables timely anomaly detection and rapid fault remediation, laying essential foundations for the availability, performance and reliability of microservice systems. Nevertheless, frequent changes of service instances and invocation topologies, noisy and sparse monitoring data, and pervasive metric drift pose significant diagnostic challenges. We present KLD, an evolution- and runtime-dependency-aware framework for joint anomaly detection and root-cause localization in dynamic microservices. Instead of treating multimodal observations as static system states, KLD explicitly models the temporal evolution of runtime signals and fault evidence propagation over execution-aware dependency structures. KLD extracts multi-order temporal and trajectory features from metrics and traces, aligns these features with log semantics via cross-modal interaction and cooperative gating, and propagates fused evidence over runtime dependency graphs reconstructed from distributed traces to model fault cascading. Leveraging margin-based contrastive representation learning, KLD distinguishes root-cause nodes corresponding to injected faults from downstream affected services. Evaluations on SN and TT datasets show that our method achieves detection F1 scores of 0.995 / 0.993 and localization HR@1 of 0.995 / 0.994, outperforming state-of-the-art baselines. Ablation studies validate the effectiveness of each component, and robustness experiments demonstrate stable performance under controlled perturbations on logs, traces and metric baselines.