CARVIEW |
Select Language
HTTP/2 200
date: Wed, 08 Oct 2025 18:05:16 GMT
content-type: text/html; charset=utf-8
cache-control: max-age=0, private, must-revalidate
cf-cache-status: DYNAMIC
link: ; rel=preload; as=style; nopush,; rel=preload; as=script; nopush,; rel=preload; as=style; nopush,; rel=preload; as=script; nopush,; rel=preload; as=script; nopush
nel: {"report_to":"heroku-nel","response_headers":["Via"],"max_age":3600,"success_fraction":0.01,"failure_fraction":0.1}
referrer-policy: strict-origin-when-cross-origin
report-to: {"group":"heroku-nel","endpoints":[{"url":"https://nel.heroku.com/reports?s=zmCpRkbrWMxTcF3zVHwg315bnfu61JC0bl6saZRhmBA%3D\u0026sid=e11707d5-02a7-43ef-b45e-2cf4d2036f7d\u0026ts=1759946716"}],"max_age":3600}
reporting-endpoints: heroku-nel="https://nel.heroku.com/reports?s=zmCpRkbrWMxTcF3zVHwg315bnfu61JC0bl6saZRhmBA%3D&sid=e11707d5-02a7-43ef-b45e-2cf4d2036f7d&ts=1759946716"
server: cloudflare
strict-transport-security: max-age=0; includeSubDomains
vary: Accept,Accept-Encoding
via: 2.0 heroku-router
x-content-type-options: nosniff
x-permitted-cross-domain-policies: none
x-request-id: eb57ce9e-118a-bd81-6387-f9c532c92566
x-runtime: 0.135715
x-xss-protection: 0
content-encoding: gzip
set-cookie: _secure_speakerd_session=ok6gifJI%2BNoktDQ4I%2Fp0%2F%2BhZnswmlctLHyPBSi4GJ9TM%2BUgG4hU5yjZhM6N%2BrpgtmArz6hc5rrzk1XEO%2FAP%2FQIn3yhXRQojrH7gtRQnKV2wfn59bctqpC4kBhV9z3s6RoqDFAekLjbzfF%2BQB3S8%2Bf8wVi8Oq0sd5FFH%2BKlgCV%2BECziPBqD20ivzZzwqDJMyuXWSjLbZq7ZpGVKlGxmwBRgUYj2%2BUhPloqgLyQB9FGjTJFYbqGu913tor4wd6r%2BK9vyBKbXm8Vca0sOXQd7fWqkl4EhD%2BImImR7R6wdG3YmPvkf949xkVDSb4cpCZDQ6tbLKg%2Flb1IKO2eLb04277U27idLoJTfKgF2bhKvGsiq9nyyZAMpOlYfCIyEMSNcfr%2FMl3FDKEsCne%2BmXgMnNgNgNX--jPRu4pzLJ%2BDH6RZ1--WdUDBc8Vs2LrtwCd0myc7A%3D%3D; HttpOnly; SameSite=Lax; Secure; Path=/; Expires=Wed, 22 Oct 2025 18:05:16 GMT
cf-ray: 98b790c24f2ec537-BLR
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 - Speaker Deck
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
More Decks by Yuuki Tsubouchi (yuuk1)
Other Decks in Research
Featured
Transcript
-
6 ੑೳҟৗΛஅ͢ΔͨΊͷطଘͷΞϓϩʔν ϝτϦοΫ ςΩετϩά ࣮ߦτϨʔε ๛ͳใΛ͕ͭϩάʹग़ྗ͞Εͳ͍ͷ ͋Δ ॲཧܦ࿏ͷล୯Ґͷεϧʔϓοτ࣮ߦ࣌ؒΛ ѲͰ͖Δ͕ɺΞϓϦέʔγϣϯʹܭଌॲཧΛ ઃఆ͢Δख͕ؒ͋Δ
ݸʑͷใྔগͳ͍͕ऩूɺอଘɺՄࢹԽ͠ ͍͢ ࣮ڥͷద༻ੑΛ౿·͑ͯɺʮϝτϦοΫʯʹண -
ɾஅʹར༻͢ΔݻఆͷϝτϦοΫΛࢦఆ͠ͳ͚ΕͳΒͳ͍ ɾΑΓݪҼʹ͍ۙϝτϦοΫ͕݁Ռ͔Βআ֎͞ΕΔՄೳੑ͕͋Δ 7 ɾߏཁૉؒΛԣஅ͢ΔϝτϦοΫؒͷҼՌؔΛਪఆ͢Δ ɾߏཁૉ୯ҐͷҼՌͷܦ࿏Λਪ͢Δ ϝτϦοΫϕʔεΞϓϩʔν Ͱ͖ΔݶΓଟ͘ͷؔ࿈͢ΔϝτϦοΫΛߴʹղੳ͢Δඞཁ͕͋Δ ՝ ଟ͘ͷϝτϦοΫΛࢹ͢Δͷ࣌ؒΛཁ͢Δ ख๏
※ ాതจ, άϥϑΟΧϧϞσϧʹجͮ͘ҼՌ୳ࡧख๏ͷௐࠪ, 2020. https://blog.tsurubee.tech/entry/2020/10/08/085158 -
8 ੑೳҟৗʹର͢ΔϝτϦοΫͷ࣍ݩݮͷఏҊ త: ϚΠΫϩαʔϏεʹ͓͍ͯɺҟৗͷܦ࿏ΛࣗಈͰਪ͢Δͨ Ίͷج൫Λఏڙ͢Δ ఏҊ: ͯ͢ͷϝτϦοΫ͔Βஅʹ༗༻ͳϝτϦοΫΛߴʹநग़͢ Δख๏ “TSifter” (Time
series Sifter) ɾᶃਖ਼֬ੑ :அʹ༗༻ͳϝτϦοΫ͕ݮ͞Ε͍ͯͳ͍ ɾᶄ࣍ݩݮ: ແ༻ͳϝτϦοΫΛͳΔ͘ଟ͘ݮ͍ͨ͠ ɾᶅߴੑ : ͳΔ͘ૣ͘ো͔Β෮چ͍ͨ͠ (ཧ1ఔ) -
9 ݪҼஅγεςϜͷશମ૾ ϝτϦοΫ औಘ ϝτϦοΫ ࣍ݩݮ ݪҼஅ ϝτϦοΫ σʔλϕʔε σʔλऩू
ҟৗݕ ఏҊख๏ͷείʔϓ YES Service A/ req_errors Service D/ connections Service E/ ҼՌͷܦ࿏ ᶃ ᶄ ᶅ ᶆ ᶇ -
11 ఏҊख๏ͷཁ݅ͱղܾ ɾᶃਖ਼֬ੑͱᶄ࣍ݩݮͷཱ྆ ɾಎ1: ҟৗൃੜલޙͰ࣌ܥྻͷ͕มԽ͠ͳ͍ϝτϦοΫ அ࣌ʹෆཁͰ͋Δ → ࣌ܥྻσʔλͷఆৗੑݕఆ ɾಎ2: ಉҰαʔϏεͷྨࣅͷ࣌ܥྻมԽͷܗঢ়ΛͭϝτϦο
Ϋ܈ҟৗͷஅ࣌ʹͰ͋Δ → ࣌ܥྻΫϥελϦϯά ɾᶅߴੑ ɾΫϥελϦϯάॲཧͷߴԽͷͨΊʹࣄલʹϝτϦοΫΛϑΟϧλ Ϧϯά͢Δ -
12 TSifter: 2ஈ֊ͷ࣍ݩݮख๏ ɾɾɾ ɾɾɾ ɾɾɾ 4UFQ 'JMUFSJOH 4UFQ $MVTUFSJOH
ऩूͨ͠ϝτϦοΫ ඇఆৗͳϝτϦοΫ ΫϥελԽ͞Εͨ ϝτϦοΫ 3FQSFTFOUBUJWFNFUSJD ҟৗظؒ ظͷࣗݾ૬ؔ पظతมಈ ϗϫΠτϊΠζ ΫϥελϦϯάޙʹ දϝτϦοΫબ -
14 ɾ֤ϚΠΫϩαʔϏεͰɺಉ༷ͷมಈΛͭϝτϦοΫΛΫϥ ελϦϯάޙɼ֤ΫϥελͷදϝτϦοΫΛҰͭબ ɾදϝτϦοΫ: ଞͷϝτϦοΫͱͷڑͷ૯͕࠷খͷͷ ɾܗঢ়ͷྨࣅੑΛߟྀͨ͠ڑईͱͯ͠ shape-based distance(SBD) Λ࠾༻ (2ͭͷܥྻΛεϥΠυͤͯ͞૬ؔΛΈΔ)
ɾߴԽͷͨΊʹɺ1ճͷΫϥελϦϯάॲཧͰΫϥελΛܾఆՄ ೳͳ֊తΫϥελϦϯάΛ࠾༻ εςοϓ2: ϝτϦοΫؒͷܗঢ়ྨࣅੑʹண -
16 ࣮ݧઃఆ ɾΞϓϦέʔγϣϯ: Sock Shop ɾςετϕου: GKE্ʹߏங ɾϝτϦοΫऩू: Prometheus ɾෛՙੜ:
Locust ɾނোೖ ɾCPUෛՙ: stress-ng ɾωοτϫʔΫԆ: tc ϋʔυΣΞߏ༧ߘΛࢀর Sock Shopͷߏਤ https://microservices-demo.github.io/ -
17 ϕʔεϥΠϯख๏: Sieve [Thalheim 17] ɾεςοϓ1: ࢄͷখ͍͞ϝτϦοΫΛऔΓআ͘ ɾεςοϓ2: ࣌ܥྻΫϥελϦϯάख๏k-ShapeʹΑΓΫϥελϦϯ άͨ͠ͷͪʹදϝτϦοΫΛબग़͢Δ
[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༻ՄೳͳγεςϜͷಛΛநग़͢Δ͜ͱ͕తͰ͋Γɺຊ ݚڀͱత͕ҟͳΔ͕ɺҟͳΔతʹԠ༻Ͱ͖ΔՄೳੑ͕͋Δ -
20 ɾTSifterͷ࣮ߦ࣌ؒϕʔεϥΠϯͷ270ഒҎ্Ͱ͋ͬͨ ɾ͍ͣΕͷख๏CPUίΞʹର࣮ͯ͠ߦ࣌ؒεέʔϧͨ͠ ߴੑͷධՁ: CPUίΞʹର͢Δ࣮ߦ࣌ؒ 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 -
21 ɾ͍ͣΕͷख๏ϝτϦοΫ͕૿େʹରͯ͠ઢܗʹεέʔϧͨ͠ ߴੑͷධՁ: ϝτϦοΫʹର͢Δ࣮ߦ࣌ؒ 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