English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring thereliability of microservice systems. However, performing RCA on modernmicroservice systems can be challenging due to their large scale, as theyusually comprise hundreds of components, leading significant human effort. Thispaper proposes TraceDiag, an end-to-end RCA framework that addresses thechallenges for large-scale microservice systems. It leverages reinforcementlearning to learn a pruning policy for the service dependency graph toautomatically eliminates redundant components, thereby significantly improvingthe RCA efficiency. The learned pruning policy is interpretable and fullyadaptive to new RCA instances. With the pruned graph, a causal-based method canbe executed with high accuracy and efficiency. The proposed TraceDiag frameworkis evaluated on real data traces collected from the Microsoft Exchange system,and demonstrates superior performance compared to state-of-the-art RCAapproaches. Notably, TraceDiag has been integrated as a critical component inthe Microsoft M365 Exchange, resulting in a significant improvement in thesystem's reliability and a considerable reduction in the human effort requiredfor RCA.

TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems