$30 off During Our Annual Pro Sale. View Details »

博士課程での研究まとめ 2023年1月版 / Summary of my research in the PhD course

博士課程での研究まとめ 2023年1月版 / Summary of my research in the PhD course

Yuuki Tsubouchi (yuuk1)

February 13, 2023
Tweet

More Decks by Yuuki Tsubouchi (yuuk1)

Other Decks in Research

Transcript

  1. ത࢜՝ఔ·ͱΊ

    2023೥1݄൛
    ژ౎େֶେֶӃ ৘ใֶݚڀՊ ஌ೳ৘ใֶઐ߈


    ௶಺ ༎थ

    View Slide

  2. Ϋϥ΢υܕΞϓϦέʔγϣϯͷߴՄ؍ଌੑ

    ʹؔ͢Δݚڀ
    ݚڀ୊໨
    Studies on High Observability of Cloud Applications
    Ϋϥ΢υܕΞϓϦέʔγϣϯͷ؍ଌγεςϜ

    ʹ͓͚Δܭଌɾอଘɾղੳͷෛՙʹؔ͢Δݚڀ
    A Study on Loads of Measurement, Storage, and Analysis

    in Observation Systems for Cloud Applications
    ީิᶃ
    ީิᶄ

    View Slide

  3. 3
    ৘ใγεςϜʹ͓͚Δʮ৴པੑʯ
    ෳࡶͳγεςϜʹର͢Δߴස౓ͷมߋͱߴ৴པੑΛཱ྆͢ΔͨΊͷ

    ޻ֶతΞϓϩʔν͕ඞཁͱ͞Ε͍ͯΔ
    ݱࡏͷ৘ใγεςϜ͸ɺΠϯλʔωοτΛհͨ͠Ϋϥ΢υίϯϐϡʔςΟϯά
    ʹΑΔఏڙ͕ҰൠతͰ͋Δ
    Ϋϥ΢υ
    Ϧιʔεڞ༗ɺ޿ҬωοτϫʔΫɺҟछι
    ϑτ΢ΣΞ/ϋʔυ΢ΣΞɺͦΕΒͷෳࡶ
    ͳ૬ޓ࡞༻Λ੒͢γεςϜ
    Πϯλʔωοτ
    Ϋϥ΢υܕ

    ΞϓϦέʔγϣϯ
    ઌ୺اۀͰ͸ɺ1೔ෳ਺ճҎ্ͷػೳมߋ
    [Humble+, 2018] Accelerate: The Science of Lean Software and DevOps: Building and scaling high performing technology organizations
    [Beyer+, 2016] Site Reliability Engineering: How Google Runs Production Systems
    [Humble+, 2018]
    [Beyer+, 2016]

    View Slide

  4. 4
    2. ӡ༻ऀʹΑΓએݴ͞Εͨ๬·͠
    ͍ঢ়ଶʹ௥ै͢Δࣗಈ੍ޚ
    3. ӡ༻ऀʹΑΔखಈ੍ޚ
    Ϋϥ΢υͷނোɾো֐ʹର͢Δ੍ޚߏ଄Ϟσϧ
    • ίϯϙʔωϯτ΍௨৴ϨϕϧͰͷ

    ނো΍ྼԽରԠ


    • ఻ૹ੍ޚɾܦ࿏੍ޚɺ෼ࢄ߹ҙͳͲ
    • ܭࢉػΫϥελͷ

    ల։ɾࣗಈ৳ॖɾ؅ཧ


    • OpenStackɺKubernetes

    ͳͲͷΦʔέετϨʔλʔ
    • ৴པੑͷ໨ඪ஋Λຬͨ͢Α͏ʹ

    ো֐ʹରͯ͠खಈͰରԠ


    • ༧๷ɾ༧ଌɾݕ஌ɾݪҼ਍அɾ

    ճ෮ɾࣄޙ෼ੳɾ࠶ൃ๷ࢭ
    ຊݚڀͰ͸ɺ3.ӡ༻ऀʹΑΔखಈ੍ޚ ʹண໨͢Δ
    Service-level
    Component-level
    System-level
    1. ϓϩτίϧʹ

    جͮࣗ͘ಈ੍ޚ
    ʢϑΥʔϧττϨϥϯεʣ

    View Slide

  5. 5
    ੍ޚͷͨΊͷ಺෦ঢ়ଶͷ؍ଌٕज़
    ඃ؍ଌγεςϜ
    σʔλετΞ
    ӡ༻ऀ
    ؍ଌγεςϜ
    ܭଌث
    ՄࢹԽ
    OSɾΞϓϦέʔγϣϯ

    ͷܭ૷ʹΑΓܭଌՄೳ
    ΞϓϦέʔγϣϯ
    ӡ༻σʔλ
    ੍ޚ
    ઐ༻ͷ؍ଌγεςϜ͕ӡ༻σʔλΛܭଌɾอଘɾՄࢹԽ [Hauser+, CLOUD2018]
    ܯใ
    ӡ༻ऀ͕σʔλ͔Β಺෦ঢ়ଶΛཧղ

    Ͱ͖ΔΑ͏ʹ͢Δ=Մ؍ଌੑΛ΋ͨͤΔ

    View Slide

  6. 6
    ӡ༻σʔλࣗಈղੳٕज़ʹΑΔՄ؍ଌੑͷ޲্
    ӡ༻σʔλετΞ ӡ༻ऀ
    ো֐༧ଌ
    [Notaro+, TIST2021]: A Survey of AIOps Methods for Failure Management.


    [Soldani+, CSUR2022]: Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey
    [Notaro+, TIST2021]


    [Soldani+, CSUR2022]
    ӡ༻σʔλࣗಈղੳث
    ো֐ݕ஌
    ো֐ͷނোՕॴಛఆ
    ʢ౷ܭɾػցֶशʣ
    ਺஋ͷ࣌ܥྻσʔλ
    ωοτϫʔΫ௨৴ͷґଘؔ܎
    [Kim+, PER2013]
    [Chen+, INFOCOM2014]
    [Lin, ICSOC2018]
    [Qiu+, Applied Science2020]
    [Wu+, NOMS2020]
    [Aggarwal+, CLOUD2021]
    ΞϓϦέʔγϣϯίʔυͷ

    վมͳ͠Ͱܭ૷Մೳͳӡ༻σʔλ
    ϝτϦΫε
    ίʔϧάϥϑ
    ࣌ؒɾۭؒσʔλΛ

    ༻͍ͨղੳख๏

    View Slide

  7. 7
    ӡ༻σʔλ૿େͷ໰୊
    ΞϓϦέʔγϣϯ ղੳث
    σʔλετΞ
    ղੳෛՙ㽉
    ܭଌෛՙ㽉 อଘෛՙ㽉
    ಛʹো֐ൃੜ࣌ʹ͸

    ୹࣌ؒͰͷղੳ͕ٻΊΒΕΔ
    ΞϓϦέʔγϣϯ͕େن໛ԽɾෳࡶԽ͢ΔʹͭΕͯɺඞཁͳӡ༻σʔλ͕૿େ


    ӡ༻σʔλͷ૿େʹΑΓɺܭଌɾอଘɾղੳෛՙ͕૿େ
    ӡ༻σʔλͷ

    ࣍ݩ਺ͷ૿େ
    ӡ༻σʔλͷॻ͖ࠐΈճ਺
    ͱอଘྔͷ૿େ
    ܭଌ࣌ͷσʔλ

    ॲཧෛՙͷ૿େ

    View Slide

  8. 8
    ݚڀ໨త
    ӡ༻σʔλͷ૿େͷࡍʹൃੜ͢Δɺ؍ଌγεςϜʹΑΔ
    ܭଌɾอଘɾղੳͷ֤ෛՙͷݦஶͳ՝୊Λղܾ
    ৚݅ ޿͘ීٴ͢Δٕज़ͷ࿮૊ΈͷதͰ՝୊Λղܾ͢Δ͜ͱʹΑΓɺӡ༻ऀ
    ΁ͷ௥Ճͷӡ༻ෛ୲Λܰݮ͢Δ
    ܭଌෛՙ อଘෛՙ ղੳෛՙ
    ίʔϧάϥϑΛܭଌ͢Δ
    ࡍͷCPUෛՙΛ௿ݮͤ͞
    Δख๏ͷఏҊ
    ϝτϦΫεͷૠೖෛՙͷ
    ௿ݮͱอଘظؒͷ௕ظԽ
    Λཱ྆͢ΔΞʔΩςΫ
    νϟͷఏҊ
    ϝτϦΫε਺ͷ૿େʹର
    ͯ͠ɺߴ଎ʹ໰୊ۭؒΛ
    ॖখ͢Δख๏ΛఏҊ
    ݚڀ՝୊̍ɿ ݚڀ՝୊̎ɿ ݚڀ՝୊̏ɿ

    View Slide

  9. 9
    ֶज़తߩݙ
    Ϋϥ΢υܕΞϓϦέʔγϣϯͱಉ༷ʹɺมԽͷස౓͕ߴ͘ɺӡ༻ऀ
    ʹΑΔதԝूݖతͳ੍ޚ͕ཁٻ͞ΕΔγεςϜͷ؅ཧख๏ͱͯ͠޿
    ͘Ԡ༻Ͱ͖ΔՄೳੑ͕͋Δ
    ɾΫϥ΢υܕΞϓϦέʔγϣϯ͕ߴՄ؍ଌੑΛ΋ͭͨΊʹඞཁͳܭଌɾอଘɾ
    ղੳͷجຊ֓೦ͱෛՙʹؔ͢Δ՝୊Λମܥతʹ੔ཧ


    ɾ֤՝୊Λٕज़తʹղܾ͢Δख๏ΛఏҊ͢Δ͜ͱʹΑΓղܾ

    View Slide

  10. 10
    [ݚڀ՝୊1] ίʔϧάϥϑͷܭଌෛՙ
    ௨৴ͷґଘΛܭଌ͢ΔͨΊʹ͸ɺ௨৴ܦ࿏Λ๣ड͢Δඞཁ͕͋Δ
    ιέοτϨϕϧ
    ύέοτϨϕϧ
    αʔό΍εΠον্Ͱ
    パ
    έοτΛ๣ड͠ɺϔομ಺ͷૹड৴ΞυϨεͱ
    ϙʔτ൪߸͔ΒґଘΛൃݟ͢Δ
    αʔόͷOSΧʔωϧ಺ͰTCP/UDPͷ௨৴ܦ࿏ͷऴ୺ʢιέοτʣʹର͢Δ
    ΠϕϯτΛܭଌ͢Δ
    ୯ҰͷTCP઀ଓ͸ෳ਺ͷύέοτͷϥ΢ϯυτϦοϓͰߏ੒͞ΕΔͨΊɺ
    ιέοτϨϕϧͷ΄͏͕ܭଌෛՙ͕௿͍
    ຊݚڀͰண໨

    View Slide

  11. Y. Tsubouchi, et al., Low Overhead TCP/UDP Socket-based Tracing for Discovering Network Services Dependencies, Journal of Information Processing 2022.
    [ݚڀ՝୊1] ఏҊख๏
    .


    .


    .
    Kernel
    User
    Service
    Socket
    Tracing

    Process
    … Event
    Event
    Event
    ετϦʔϛϯά๏(Weave Scope)
    ϑϩʔू໿๏ ([Datadog], [SAC 20])
    ϑϩʔूଋ๏ʢఏҊʣ
    .


    .


    .
    Kernel
    Service
    Socket
    Tracing

    Process
    .


    .


    .
    Event
    Flow
    Event
    Event
    Event


    .


    .


    . .


    .


    .
    User
    Service
    Socket
    Tracing

    Process
    .


    .


    .
    ✗ ΧʔωϧˠϢʔβۭؒؒ
    ͷΠϕϯτͷίϐʔίετ
    ✗ TCP઀ଓϨʔτ͕૿Ճ͢Δ
    ͱɺίϐʔίετ͕૿Ճ ෳ਺ͷϑϩʔΛूଋ
    ϑϩʔ= ྆୺ͷΞυϨεͱϙʔτͷ
    ૊ʢλϓϧʣ͕ಉҰͷ௨৴୯Ґ
    Event
    Event …

    Event
    Event
    .


    .


    .
    Event
    Event …
    Event
    Event
    .


    .


    .
    Ұൠతʹ௨৴͸OSΧʔωϧͷTCP/UDPΛ࢖༻͢Δ͜ͱ
    ʹண໨
    11

    View Slide

  12. 12
    ɾఏҊख๏͸ɺCPUར༻཰͸2.2%ҎԼɻ


    ɾϑϩʔ਺ͷ૿େʹରͯ͠ɺϑϩʔूଋʹΑΓɺ

    CPUར༻཰Λ௿͘ҡ͍࣋ͯ͠Δ
    [ݚڀ՝୊1] ϑϩʔ਺ͷ૿େʹର͢ΔCPU࢖༻ྔͷมԽ
    ఏҊख๏
    Y. Tsubouchi, et al., Low Overhead TCP/UDP Socket-based Tracing for Discovering Network Services Dependencies, Journal of Information Processing 2022.
    0
    5
    10
    15
    20
    25
    5 10 15 20 25 30 35
    CPU usage / core (%)
    TCP round trips / sec (x103)
    Streaming(client)
    Streaming(server)
    In-Kernel-Aggr(client)
    In-Kernel-Aggr(server)
    In-Kernel-Bundling(client)
    In-Kernel-Bundling(server)

    View Slide

  13. 13
    [ݚڀ՝୊2] ϝτϦΫεͷอଘෛՙ
    ɾ෺ཧɾ࿦ཧࢿݯ࢖༻ɿCPUɾϝϞϦɾωοτϫʔΫɾσΟεΫɺϓϩηεɾεϨου਺౳


    ɾཁٻൃߦɿԠ౴஗Ԇ࣌ؒɾεϧʔϓοτɾΤϥʔ਺౳
    ϝτϦΫεͷ

    σʔλετΞ
    ΞϓϦέʔγϣϯ
    ϝτϦΫεͷྫ
    5-60ඵ

    ִؒ
    300-500೔ఔ౓ͷ

    อଘظؒ
    ਺ඦ͔Β਺ेສݸͷ

    ܭଌର৅

    View Slide

  14. 14
    [ݚڀ՝୊2] ϝτϦΫεͷॻ͖ࠐΈճ਺ͱอଘྔͷߴޮ཰Խ
    ϝϞϦ

    ϕʔεDB
    σΟεΫ

    ϕʔεDB
    σʔλ఺

    ૠೖ
    dݸͷσʔλ఺Λ

    ஝ੵޙόονॻ͖ࠐΈ
    ఏҊɿೋछDBͷ֊૚Խ
    M

    (/s)
    M / d

    (/s)
    σΟεΫϕʔε


    ෼ࢄDB
    ϝϞϦϕʔε

    ෼ࢄDB
    ࡧҾߏ଄͕ฏߧ໦

    ϝτϦΫε਺nͱ͢Δͱ
    O(log n)ͷܭࢉྔ
    ϝϞϦ

    ϕʔεDB
    ϋογϡද O(k)
    ฏߧ໦ O(log n)
    σΟεΫ

    ϕʔεDB
    OpenTSDB(HBase)


    KairosDB(Cassandra)



    Redis

    ࡧҾߏ଄͕ϋογϡද

    O(n)ͷܭࢉྔ
    ॻ͖ࠐΈෛՙ͸ϝϞϦϕʔε
    DB͕୲͍ɺσʔλͷ௕ظอଘ
    ͸σΟεΫϕʔεDB͕୲͏


    ௕ظอଘ޲͖ ௕ظอଘෆ޲͖

    View Slide

  15. 15
    [ݚڀ՝୊2] ܥྻ਺ͷ૿Ճʹର͢ΔૠೖεϧʔϓοτมԽ
    ɾσΟεΫϕʔεDBͷΈͱൺֱ͠ɺ
    ࠷େͰ3.96ഒͷεϧʔϓοτ


    ɾεϧʔϓοτͷ௿Լ཰΋վળͨ͠
    0
    20
    40
    60
    80
    100
    100 1K 10K 100k 1M
    0
    20
    40
    60
    Insertion throughput (kilo datapoints / sec)
    Throughput decrease rate (%)
    The number of series
    HeteroTSDB (Proposed)
    KairosDB
    HeteroTSDB (Proposed)
    KairosDB
    ϝϞϦDB → σΟεΫDBͷҠಈεϧʔ
    ϓοτͱϝϞϦDB΁ͷεϧʔϓοτͱ
    ಉఔ౓
    0
    20
    40
    60
    80
    100
    0 300 600 900 1200 1500 1800
    0
    500
    1000
    1500
    2000
    Insertion throughput (kilo datapoints / sec)
    Memory used size (MB)
    Elapsed time (sec)
    Flushed datapoints (/sec)
    Memory used size (MB)

    View Slide

  16. 16
    [ݚڀ՝୊3] ϝτϦΫεͷղੳෛՙͷ௿ݮͷ՝୊
    ɾো֐ݕ஌ޙͷ୹࣌ؒͰͷނোՕॴಛఆͷͨΊʹɺଟ࣍ݩϝτϦΫεͷ࣍ݩ࡟
    ݮख๏ʹண໨
    จݙ໊ ϝτϦΫε਺ ࣍ݩ࡟ݮʢख๏ʣ ނোՕॴಛఆ
    [PatternMatcher


    21]
    260K ҟৗݕ஌ʢKSݕఆʣ
    CNNʹΑΔ


    ҟৗύλʔϯ෼ྨ +
    ҟৗ౓ϥϯΩϯά
    [FluxInfer 20] 12K ҟৗݕ஌ʢࠞ߹ਖ਼ن෼෍ʣ
    ҼՌάϥϑ +
    PageRank
    [FluxRank 19] 541K
    ҟৗݕ஌ʢΧʔωϧີ౓ਪఆʣ

    ΫϥελϦϯάʢDBSCANʣ
    ϥϯΩϯάֶश
    ɾ ʮඇܧଓதͷҟৗʯ΍ʮॠؒతͳҟৗʯΛਖ਼ৗͱ෼ྨ͢Δِཅੑ͋Γ


    ɾ·ͨ͸ɺϝτϦΫεͷछྨ͝ͱͷௐ੔͕ඞཁ

    View Slide

  17. ࣌ܥྻͷपظͰ͸ͳ͘ɺزԿతͳҟ
    ৗͷಛ௃Λଊ͑Δख๏ͷఏҊ
    17
    [ݚڀ՝୊3] ࣍ݩ࡟ݮ๏ͷϑϨʔϜϫʔΫԽ
    ো֐

    ݕ஌
    ނোՕॴ

    ಛఆ
    ࣍ݩ࡟ݮ
    ௶಺༎थ΄͔, TSifter: ϚΠΫϩαʔϏεʹ͓͚Δੑೳҟৗͷਝ଎ͳ਍அʹ޲͍ͨ࣌ܥྻσʔλͷ࣍ݩ࡟ݮख๏, Πϯλʔωοτͱӡ༻ٕज़γϯϙδ΢Ϝ࿦จू, 2020೥.
    ఏҊɿނোՕॴಛఆ޲͚ͷ࣍ݩ࡟ݮ๏Λෳ਺ͷϑΣʔζʹ෼ྨ͠ɺطଘͷख๏
    ͷِཅੑͷ՝୊Λղܾ͢Δ
    ϑΣʔζ1ɿΦϑϥΠϯҟৗݕ஌
    ࣌ܥྻͷܗঢ়ͷྨࣅੑʹج͍ͮͨΫ
    ϥελϦϯάΛ࣮ߦ


    ֤Ϋϥελͷॏ৺ͱͳΔ୅දϝτϦ
    ΫεΛநग़
    ϑΣʔζ̎ɿܗঢ়ྨࣅੑΫϥελϦϯά

    View Slide

  18. 18
    [ݚڀ՝୊3] ࠶ݱ཰ɺϝτϦΫε࡟ݮੑೳɺߴ଎ੑͷධՁ
    ʮ࣍ݩ࡟ݮʯˠʮނোՕॴಛఆʯͷ౷߹ධՁ
    ɾ࣍ݩ࡟ݮͳ͠ɺطଘͷ࣍ݩ࡟ݮ๏ͱൺֱ͠ɺTop-k ͷಛఆੑೳ͕ʓʓ%޲্


    ɾϝτϦΫε਺Λ1k ͔Β10kʹ૿Ճͯ͠΋ɺطଘख๏ͱൺֱ͠ɺಛఆੑೳͷ
    ௿Լ཰͕খ͍͞
    σʔλ

    ηοτ
    খن໛ɿϝτϦΫε਺ 1k ݸ


    େن໛ɿϝτϦΫε਺ 10k ݸ
    ʮ࣍ݩ࡟ݮʯͷ෦ҐධՁ
    ɾϑΣʔζ̍ɿఏҊख๏ͷ࠶ݱ཰ʢRecallʣ͕97%


    ɾϑΣʔζ̎ɿະ੔ཧ
    εϥΠυ࡞੒࣌఺Ͱ͸ɺطଘͷނোՕॴಛఆख
    ๏͕खݩͷσʔληοτͰ͸ඇৗʹ௿͍ಛఆੑ
    ೳΛࣔ͢͜ͱʹ೰·͞Ε͍ͯΔ

    View Slide

  19. 19
    ݁࿦
    Ϋϥ΢υܕΞϓϦέʔγϣϯͷՄ؍ଌੑΛ޲্ͤ͞Δࡍͷӡ༻σʔλྔͷ૿େ
    ʹର͢ΔܭଌɾอଘɾղੳͷෛՙΛ௿ݮͤ͞Δख๏ΛఏҊͨ͠
    อଘ


    ෛՙ
    ղੳ


    ෛՙ
    ܭଌ


    ෛՙ
    ɾίʔϧάϥϑͷܭଌʹண໨͠ɺLinuxΧʔωϧ಺ͷιέοτͷΠϕϯτΛϑϩʔ୯
    ҐͰऩଋ͢Δܭ૷ํࣜΛఏҊͨ͠


    ɾಛʹTCP୹໋઀ଓ͕ଟ͍؀ڥͰͷCPUར༻཰Λ௿ݮͤͨ͞
    ɾϝτϦΫε਺ͷ૿େʹରͯ͠ɺϝϞϦϕʔεDBͱσΟεΫϕʔεDBͷࡧҾߏ
    ଄ͷࠩҟʹண໨͠ɺೋछͷDBΛ֊૚Խ͢ΔΞʔΩςΫνϟΛఏҊͨ͠


    ɾσΟεΫϕʔεDBͷΈͱൺֱ͠ɺ࠷େͰ3.96ഒͷεϧʔϓοτ
    ɾ࣍ݩ਺ʢϝτϦΫε਺ʣͷ૿େʹରͯ͠ɺҟৗݕ஌ͱ࣌ܥྻͷྨࣅ౓ΫϥελϦ
    ϯάͷೋஈ֊ͷ࣍ݩ࡟ݮ๏ΛఏҊͨ͠


    ɾ࠶ݱ཰Λʓʓ%ɺ˚˚%ͷ࣍ݩ࡟ݮ཰Λୡ੒ͨ͠

    View Slide

  20. 20
    ത࢜՝ఔͷৼΓฦΓ
    ɾܭըํࣜ̍ɿ͋ΔҰͭͷఏҊͷ࣮ݱʹ޲͚ͯɺෳ਺ͷ࿈ଓͨ͠՝୊Λநग़͠ɺղܾ


    ɾܭըํࣜ̎ɿ࣮༻্ͷಠཱͨ͠՝୊Λ1ͭͣͭղܾͨ͠ޙʹڞ௨߲ͱҐஔ͚ͮΛ੔ཧ


    ɾຊݚڀͰ͸ɺํࣜ̎Λ݁Ռతʹ࠾༻


    ɾૣظͷ࣮༻ԽΛ໨ࢦ͢΄Ͳɺղܾͷ׬શੑ͕ٻΊΒΕΔͷͰɺ՝୊ಉ͕࢜ܨ͕Γʹ͍͘
    ത࢜՝ఔͷݚڀܭը
    ɾ࡞ۀޮ཰


    ɾจݙͱ࣮ݧ؀ڥΛ՝୊ؒͰڞ༗Ͱ͖ͳ͍ͱɺ࡞ۀྔ͕՝୊਺෼͚ͩ૿େ͢Δ


    ɾຊݚڀͰ͸ɺશ͘ҟͳΔ࣮ݧ؀ڥΛ3ճߏஙͨ͠


    ɾݚڀͷෆ࣮֬ੑ΁ͷରԠ


    ɾ՝୊͕͢ͰʹطଘݚڀͰղܾ͞ΕͨͳͲɺ్தͰ࠳ં͢Δ͜ͱ͕͋Δ


    ɾຊݚڀͰ͸ɺͲͷ՝୊ʹऔΓ૊Ή͔Ͱ໎͏࣌ؒ͸গͳ͔ͬͨ
    ܭըํࣜ̍ͷ΄͏͕͓ͦΒ͘༗ར
    ܭըํࣜ̎ͷ΄͏͕͓ͦΒ͘༗ར

    View Slide

  21. 21
    ɾ Y. Tsubouchi, M. Furukawa, R. Matsumoto, Low Overhead TCP/UDP Socket-based Tracing for Discovering
    Network Services Dependencies, Journal of Information Processing (JIP), Vol.30, pp.260-268, 2022೥3݄.
    ͜Ε·Ͱͷݚڀۀ੷: ओͳࠪಡ෇͖࿦จ
    δϟʔφϧ࿦จ
    ࠃࡍձٞ
    ɾ Y. Tsubouchi, M. Furukawa, R. Matsumoto, Transtracer: Socket-Based Tracing of Network Dependencies
    among Processes in Distributed Applications, The 1st IEEE International COMPSAC Workshop on
    Advanced IoT Computing (AIOT 2020), July 2020.
    ࠃ಺γϯϙδ΢Ϝ
    ɾ ௶಺༎थ, ࿬ࡔேਓ, ᖛా݈, দ໦խ޾, খྛོߒ, Ѩ෦ത, দຊ྄հ, HeteroTSDB: ҟछ෼ࢄKVSؒͷࣗಈ֊૚Խ
    ʹΑΔߴੑೳͳ࣌ܥྻσʔλϕʔε, ৘ใॲཧֶձ࿦จࢽ, Vol.62, No.3, pp.818-828, 2021೥3݄.
    ɾ Y. Tsubouchi, A. Wakisaka, K. Hamada, M. Matsuki, H. Abe, R. Matsumoto, HeteroTSDB: An Extensible Time
    Series Database for Automatically Tiering on Heterogeneous Key-Value Stores, The 43rd Annual IEEE
    International Computers, Software & Applications Conference (COMPSAC), pp. 264-269, July 2019.
    ɾ ௶಺༎थ, ௽ాതจ, ݹ઒խେ, TSifter: ϚΠΫϩαʔϏεʹ͓͚Δੑೳҟৗͷਝ଎ͳ਍அʹ޲͍ͨ࣌ܥྻσʔλ
    ͷ࣍ݩ࡟ݮख๏, Πϯλʔωοτͱӡ༻ٕज़γϯϙδ΢Ϝ࿦จू, 2020, 9-16 (2020-11-26), 2020೥12݄.
    ɾ ௶಺༎थ, ҏ໺จ඙, ஔాਅੜ, ࢁ઒૱, ദ໦ַ඙, ഡݪ݉Ұ, ॏෳഉআετϨʔδͷͨΊͷSHA-1
    ܭࢉγεςϜͷSSE໋ྩʹΑΔߴεϧʔϓοτԽ, ిࢠ৘ใ௨৴ֶձ࿦จࢽ D, 96(10), pp.2101-2109 2013೥10݄.
    ʢത࢜࿦จʹؚΊͣʣ
    ɾ ௶಺༎थ, ੨ࢁਅ໵, MeltriaɿϚΠΫϩαʔϏεʹ͓͚Δҟৗݕ஌ɾݪҼ෼ੳͷͨΊͷσʔληοτͷಈతੜ੒
    γεςϜ, Πϯλʔωοτͱӡ༻ٕज़γϯϙδ΢Ϝ࿦จू, 2021, 63-70 (2021-11-18), 2021೥11݄.

    View Slide

  22. 22
    ͜Ε·Ͱͷݚڀۀ੷ɿͦͷଞ
    ࠃ಺ߨԋ
    ɾ ௶಺༎थ, ௽ాതจ, AI࣌୅ʹ޲͚ͨΫϥ΢υʹ͓͚Δ৴པੑΤϯδχΞϦϯάͷະདྷߏ૝, ϚϧνϝσΟΞɺ
    ෼ࢄɺڠௐͱϞόΠϧʢDICOMO2022ʣγϯϙδ΢Ϝ, 2022೥7݄14೔.
    ɾ ௶಺༎थ, AIOpsݚڀ࿥ʕSREͷͨΊͷγεςϜো֐ͷࣗಈݪҼ਍அ, SRE NEXT 2022 ONLINE, 2022೥5݄.
    ࠃ಺ձٞ࿥ʢࠪಡͳ͠ʣ
    ɾ ྛ༑Ղ, দݪࠀ໻, ࿯๺ݡ, ௶಺༎थ, ϚΠΫϩαʔϏεܕγεςϜͷ؂ࢹʹ͓͚ΔμογϡϘʔυUIઃܭʹىҼ
    ͢Δঢ়گೝࣝ΁ͷӨڹ, No.2022-IOT-56, Vol.38, pp.1-8, 2022೥3݄.
    ɾ দຊ྄հ, ௶಺༎थ, ΫϥΠΞϯτϓϩηεͷݖݶ৘ใʹجͮ͘TCPΛհͨ͠ಁաతͳݖݶ෼཭ํࣜͷઃܭ, ৘
    ใॲཧֶձݚڀใࠂΠϯλʔωοτͱӡ༻ٕज़ʢIOTʣ, No.2020-IOT-49, Vol.11, pp.1-6, 2020೥5݄.
    ɾ ྛ༑Ղ, ҏ੎ా࿇, দݪࠀ໻, ࿯๺ݡ, ௶಺༎थ, দຊ྄հ, ಈతదԠੑΛ࣋ͭ෼ࢄγεςϜΛର৅ͱͨ͠γεςϜ
    ঢ়ଶՄࢹԽख๏ͷݕ౼, ৘ใॲཧֶձݚڀใࠂΠϯλʔωοτͱӡ༻ٕज़ʢIOTʣ, No.2020-IOT-48, Vol.22,
    pp.1-8, 2020೥3݄.
    ɾ ௶಺༎थ, ෼ࢄΞϓϦέʔγϣϯͷ৴པੑ؍ଌٕज़ʹؔ͢Δݚڀ, SRE NEXT 2020 IN TOKYO, 2020೥1݄25೔

    View Slide