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マイクロサービスにおける性能異常の迅速な診断に向いた時系列データの次元削減手法 / Dimen...

マイクロサービスにおける性能異常の迅速な診断に向いた時系列データの次元削減手法 / Dimention Reduction of Time Series Data in Microservices

第7回WebSystemArchitecture研究会(WSA研)
https://wsa.connpass.com/event/187128/

Yuuki Tsubouchi (yuuk1)

November 13, 2020
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  1. 8 ੑೳҟৗʹର͢ΔϝτϦοΫͷ࣍ݩ࡟ݮͷఏҊ ໨త: ϚΠΫϩαʔϏεʹ͓͍ͯɺҟৗͷ఻೻ܦ࿏ΛࣗಈͰਪ࿦͢Δͨ Ίͷج൫Λఏڙ͢Δ ఏҊ: ͢΂ͯͷϝτϦοΫ͔Β਍அʹ༗༻ͳϝτϦοΫΛߴ଎ʹநग़͢ Δख๏ “TSifter” (Time

    series Sifter) ɾᶃਖ਼֬ੑ :਍அʹ༗༻ͳϝτϦοΫ͕࡟ݮ͞Ε͍ͯͳ͍ ɾᶄ࣍ݩ࡟ݮ཰: ແ༻ͳϝτϦοΫΛͳΔ΂͘ଟ͘࡟ݮ͍ͨ͠ ɾᶅߴ଎ੑ : ͳΔ΂͘ૣ͘ো֐͔Β෮چ͍ͨ͠ (ཧ૝͸1෼ఔ౓)
  2. 9 ݪҼ਍அγεςϜͷશମ૾ ϝτϦοΫ औಘ ϝτϦοΫ ࣍ݩ࡟ݮ ݪҼ਍அ ϝτϦοΫ σʔλϕʔε σʔλऩू

    ҟৗݕ஌ ఏҊख๏ͷείʔϓ YES Service A/ req_errors Service D/ connections Service E/ ҼՌͷܦ࿏ ᶃ ᶄ ᶅ ᶆ ᶇ
  3. 12 TSifter: 2ஈ֊ͷ࣍ݩ࡟ݮख๏ ɾɾɾ ɾɾɾ ɾɾɾ 4UFQ 'JMUFSJOH 4UFQ $MVTUFSJOH

    ऩूͨ͠ϝτϦοΫ ඇఆৗͳϝτϦοΫ ΫϥελԽ͞Εͨ ϝτϦοΫ 3FQSFTFOUBUJWFNFUSJD ҟৗظؒ ୹ظͷࣗݾ૬ؔ पظతมಈ ϗϫΠτϊΠζ ΫϥελϦϯάޙʹ ୅දϝτϦοΫબ୒
  4. 16 ࣮ݧઃఆ ɾΞϓϦέʔγϣϯ: Sock Shop ɾςετϕου: GKE্ʹߏங ɾϝτϦοΫऩू: Prometheus ɾෛՙੜ੒:

    Locust ɾނো஫ೖ ɾCPUෛՙ: stress-ng ɾωοτϫʔΫ஗Ԇ: tc ϋʔυ΢ΣΞߏ੒͸༧ߘΛࢀর Sock Shopͷߏ੒ਤ https://microservices-demo.github.io/
  5. 17 ϕʔεϥΠϯख๏: Sieve [Thalheim 17] ɾεςοϓ1: ෼ࢄ஋ͷখ͍͞ϝτϦοΫΛऔΓআ͘ ɾεςοϓ2: ࣌ܥྻΫϥελϦϯάख๏k-ShapeʹΑΓΫϥελϦϯ άͨ͠ͷͪʹ୅දϝτϦοΫΛબग़͢Δ

    [Thalheim 17] Thalheim, J., Rodrigues, A., Akkus, I. E., Bhatotia, P., Chen, R., Viswanath, B., Jiao, L. and Fetzer, C., Sieve: Actionable Insights from Monitored Metrics in Distributed Systems, the ACM/IFIP/USENIX Middleware, pp. 14–27 2017. ߃ৗతʹར༻ՄೳͳγεςϜͷಛ௃Λநग़͢Δ͜ͱ͕໨తͰ͋Γɺຊ ݚڀͱ͸໨త͕ҟͳΔ͕ɺҟͳΔ໨తʹ΋Ԡ༻Ͱ͖ΔՄೳੑ͕͋Δ
  6. 20 ɾTSifterͷ࣮ߦ࣌ؒ͸ϕʔεϥΠϯͷ270ഒҎ্Ͱ͋ͬͨ ɾ͍ͣΕͷख๏΋CPUίΞʹର࣮ͯ͠ߦ࣌ؒ͸εέʔϧͨ͠ ߴ଎ੑͷධՁ: CPUίΞ਺ʹର͢Δ࣮ߦ࣌ؒ 0 200 400 600 800

    1000 1200 1400 1 2 3 4 Execution time (sec) Number of CPU cores Clustering 1224.87 613.31 416.55 317.65 Filtering 0.17 0.17 0.17 0.17 Total 1225.04 613.48 416.72 317.82 0 1 2 3 4 1 2 3 4 Execution time (sec) Number of CPU cores Clustering 0.37 0.21 0.20 0.15 Filtering 3.57 1.81 1.26 0.99 Total 3.93 2.02 1.46 1.14 TSifter ϕʔεϥΠϯ
  7. 21 ɾ͍ͣΕͷख๏΋ϝτϦοΫ਺͕૿େʹରͯ͠ઢܗʹεέʔϧͨ͠ ߴ଎ੑͷධՁ: ϝτϦοΫ਺ʹର͢Δ࣮ߦ࣌ؒ TSifter ϕʔεϥΠϯ 0 20 40 60

    20000 40000 60000 80000 100000 Execution time (sec) Number of metrics Clustering 1.21 2.43 3.81 5.72 8.68 Filtering 10.24 20.28 31.05 42.14 54.41 Total 11.45 22.71 34.86 47.86 63.09 0 5000 10000 15000 20000 20000 40000 60000 80000 100000 Execution time (sec) Number of metrics Clustering 3908.10 7773.00 11710.26 15670.81 19590.83 Filtering 2.88 7.63 13.54 22.91 32.33 Total 3910.98 7780.63 11723.80 15693.72 19623.16