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

Cloud applications are increasingly shifting from large monolithic servicesto complex graphs of loosely-coupled microservices. Despite the advantages ofmodularity and elasticity microservices offer, they also complicate clustermanagement and performance debugging, as dependencies between tiers introducebackpressure and cascading QoS violations.  We present Sage, a machine learning-driven root cause analysis system forinteractive cloud microservices. Sage leverages unsupervised ML models tocircumvent the overhead of trace labeling, captures the impact of dependenciesbetween microservices to determine the root cause of unpredictable performanceonline, and applies corrective actions to recover a cloud service's QoS. Inexperiments on both dedicated local clusters and large clusters on GoogleCompute Engine we show that Sage consistently achieves over 93% accuracy incorrectly identifying the root cause of QoS violations, and improvesperformance predictability.

Sage: Using Unsupervised Learning for Scalable Performance Debugging in Microservices