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

Transcript

1. ത࢜՝ఔ·ͱΊ 
 2023೥1݄൛ ژ౎େֶେֶӃ ৘ใֶݚڀՊ ஌ೳ৘ใֶઐ߈ ௶಺ ༎थ
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
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]
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
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
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๏
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
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
9. 9 ֶज़తߩݙ Ϋϥ΢υܕΞϓϦέʔγϣϯͱಉ༷ʹɺมԽͷස౓͕ߴ͘ɺӡ༻ऀ ʹΑΔதԝूݖతͳ੍ޚ͕ཁٻ͞ΕΔγεςϜͷ؅ཧख๏ͱͯ͠޿ ͘Ԡ༻Ͱ͖ΔՄೳੑ͕͋Δ ɾΫϥ΢υܕΞϓϦέʔγϣϯ͕ߴՄ؍ଌੑΛ΋ͭͨΊʹඞཁͳܭଌɾอଘɾ ղੳͷجຊ֓೦ͱෛՙʹؔ͢Δ՝୊Λମܥతʹ੔ཧ ɾ֤՝୊Λٕज़తʹղܾ͢Δख๏ΛఏҊ͢Δ͜ͱʹΑΓղܾ
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໨
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
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)
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৅
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޲͖
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)
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
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
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
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੒ͨ͠
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
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.
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೔