分散システムの性能異常に対する機械学習の解釈性に基づく原因診断手法 / A Method for Diagnosing the Causes of Performance Issues in Distributed Systems Based on the Interpretability of Machine Learning
分散システムの性能異常に対する機械学習の解釈性に基づく原因診断手法 / A Method for Diagnosing the Causes of Performance Issues in Distributed Systems Based on the Interpretability of Machine Learning
ػցֶश ϝτϦοΫ (ex. CPU usage) ҟৗ • γϟʔϓϨΠɼಛྔͷ͕૿͑ΔʹͭΕͯܭࢉྔ͕େʹͳΔɽ • ଈ࣌ੑ͕ٻΊΒΕΔҟৗͷݪҼஅʹ͓͍ͯɼγϟʔϓϨΠͷܭࢉ͕ ࣮༻తͳ࣌ؒʹܭࢉՄೳͰ͋Δ͔ͷٞߦΘΕ͍ͯͳ͍ɽ γϟʔϓϨΠΛݪҼஅʹ༻͍Δࡍͷ՝ ※7 L. Antwarg et al., Explaining Anomalies Detected by Autoencoders Using SHAP, arXiv:1903.02407, 2019. ※8 N. Takeishi and Y. Kawahara, On Anomaly Interpretation via Shapley Values, arXiv:2004.04464, 2020.
্هͷཁ݅Λຬͨ͢γϟʔϓϨΠͷۙࣅख๏ͱͯ͠ɼSHAP͕ڍ͛ΒΕΔɽ • SHAPʹɼ͍͔ͭ͘ͷۙࣅΞϧΰϦζϜ͕ଘࡏ͢Δ͕ɼͦͷதͰ͋ΒΏΔػցֶशϞσϧʹ ద༻ՄೳͳKernel SHAP※6͕༗ޮͰ͋Δɽ • Kernel SHAPɼॏΈ͖ͷ࠷খೋઢܗճؼʹΑΓγϟʔϓϨΠΛۙࣅͰ͖Δɽ ఏҊख๏Ͱɼߴ͍அਫ਼Λୡ͢ΔͨΊʹɼҟৗʹର͢Δ֤ϝτϦοΫͷߩݙΛγϟʔϓϨΠ Λ༻͍ͯࢉग़͠ɼҟৗͷݪҼͱͳΔϝτϦοΫΛಛఆ͢ΔΞʔΩςΫνϟΛ࠾༻͢Δ ※6 S. Lundberg and S. I. Lee, A Unified Approach to Interpreting Model Predictions, Advances in Neural Information Processing Systems 30(NIPS 2017), 2017.