AI࣌୅ʹ޲͚ͨΫϥ΢υʹ͓͚Δ 
 ৴པੑΤϯδχΞϦϯάͷະདྷߏ૝ ௶಺༎थɹɹ௽ాതจ 2022/07/14 DICOMO 2022 ট଴ߨԋ ※1 ͘͞ΒΠϯλʔωοτݚڀॴ ※2 ژ౎େֶେֶӃ৘ใֶݚڀՊ ※̍ ※1 ※2

2 ϓϩϑΟʔϧ ௶಺ ༎थ ͘͞ΒΠϯλʔωοτݚڀॴɹݚڀһ ژ౎େֶେֶӃ৘ใֶݚڀՊɹത࢜ޙظ՝ఔ3೥ TopotalɹςΫϊϩδΞυόΠβʔ ৽ଔ͔Β5೥ؒɺגࣜձࣾ͸ͯͳͰΤϯδχΞΛ຿ΊΔ https://yuuk.io/ 2019೥ΑΓ͘͞ΒΠϯλʔωοτʹస৬͠ɺݚڀ։ൃͷੈք΁ 2020೥ʹژ౎େֶେֶӃ ത࢜ޙظ՝ఔʹೖֶ @yuuk1t Ϋϥ΢υʹ͓͚Δߴ৴པԽͷͨΊͷɺ 
 ӡ༻σʔλͷߴޮ཰ͳऩूͱɺ 
 ౷ܭղੳɾػցֶशʹجͮ͘ো֐ݪҼ਍அ ݚڀςʔϚ

ͦ͜Ͱɺ৘ใγεςϜͷ৴པੑʹؔ͢ΔΤϯδχΞϦϯάͷݱࡏ Λ੔ཧ͠ɺདྷͨΔ΂͖ະདྷͷAI࣌୅ʹ͓͚Δ৴པੑ΁ͷΞϓϩʔ νΛߏ૝͠·͢ Έͳ͞·ͷࠓޙͷݚڀͷண૝ͷछͱͯ࣋ͪ͠ؼ͍͚ͬͯͨͩΔ͜ ͱ͕͋Ε͹޾͍Ͱ͢ɻ·ͨɺຊߏ૝ΛίϛϡχςΟͰҭ͍͖ͯͯ ͍ͨͱ΋ߟ͍͑ͯ·͢ɻ اۀʹ͓͚ΔࣄۀʹؔΘΔதͰɺະདྷͷల๬Λݚڀऀͷཱ৔Ͱఏࣔ ͢Δ͜ͱͷॏཁੑ͕ߴ·͍ͬͯΔΑ͏ʹײ͍ͯ͡·͢ɻ

1. Ϋϥ΢υʹ͓͚Δ৴པੑΤϯδχΞϦϯά 2. AI࣌୅ʹ͓͚Δ৴པੑΤϯδχΞϦϯάͷະདྷ 3. AIͱͷڠಇʹΑΔ৴པੑΤϯδχΞϦϯάͷݕ౼ 4. ͓ΘΓʹ 4 ΞδΣϯμ ݱࡏɺͲ͏ͳͬͯ 
 ͍Δͷ͔ 20೥ઌͷະདྷͰ 
 Ͳ͏͋Γ͍͔ͨ ະདྷͱݱࡏͷࠩΛ 
 ຒΊΔಓے͸ͳʹ͔

1. Ϋϥ΢υʹ͓͚Δ৴པੑΤϯδχΞϦϯά 2. AI࣌୅ʹ͓͚Δ৴པੑΤϯδχΞϦϯάͷະདྷ 3. AIͱͷڠಇʹΑΔ৴པੑΤϯδχΞϦϯάͷݕ౼ 4. ͓ΘΓʹ 5 ΞδΣϯμ ݱࡏɺͲ͏ͳͬͯ 
 ͍Δͷ͔ 20೥ઌͷະདྷͰ 
 Ͳ͏͋Γ͍͔ͨ ະདྷͱݱࡏͷࠩΛ 
 ຒΊΔಓے͸ͳʹ͔

6 ৘ใγεςϜͷ৴པੑʢReliabilityʣͷॏཁੑ ɾߨԋ௚લʹ͸ɺCloud fl areʢ݄̒ʣͱKDDIʢ݄̓ʣͷͦΕͧΕͷγεςϜʹ େن໛ͳো֐͕ൃੜͨ͠ ɾো֐ʹૺ۰͢Δͱɺਓʑ͸ࣗಈԽ͞ΕͨγεςϜΛ৴པͯ͠Α͍΋ͷ͔෼͔ Βͣɺґଘ͢Δ͜ͱΛڪΕΔ ɾҰํͰɺ৘ใγεςϜͷ৴པੑΛҡ࣋͢ΔͨΊʹɺ৘ใٕज़ऀ͕೔ʑ࿑ۤΛ ॏͶ͍ͯΔ ɾࠓޙɺDX͕Ճ଎͢ΔதͰɺ৴པੑʹؔΘΔ໰୊ʹऔΓ૊Ή͜ͱ͸ॏཁͰ͋Δ [Beyer+, 2016] Site Reliability Engineering: How Google Runs Production Systems ৘ใγεςϜʹ͓͍ͯɺʮ৴པੑ͸࠷΋جຊతͳػೳʯͰ͋Δ [Beyer+, 2016]

7 ৘ใγεςϜʹ͓͚Δʮ৴པੑʯͷݱࡏ ৴པੑͷʮݱࡏʯʹͭͳ͕Δɺྺ࢙తมભΛΈ͍ͯ͘ ߴස౓ͷมߋͱߴ৴པੑΛཱ྆͢ΔͨΊͷΞϓϩʔνͷීٴ͕ਐΜͰ͍Δ ݱࡏͷ৘ใγεςϜ͸ɺΫϥ΢υίϯϐϡʔςΟϯάʹΑΔఏڙ͕Ұൠత Ϋϥ΢υ Site Reliability EngineeringʢSREʣ Ϧιʔεڞ༗ɺ޿ҬωοτϫʔΫɺҟ छιϑτ΢ΣΞ/ϋʔυ΢ΣΞɺͦΕ Βͷෳࡶͳ૬ޓ࡞༻Λ੒͢γεςϜ Πϯλʔωοτ Infrastructure Platform Application ઌ୺اۀͰ͸ɺ1೔ෳ਺ճҎ্ͷมߋ [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]

8 ৴པੑʹؔΘΔ΋ͷ͝ͱͷྺ࢙తมભ ೥୅ ৴པੑͷର৅ γεςϜͷఏڙܗଶ ߴ৴པԽͷߟ͑ํ ৴པੑͷఆٛ 1940~ 60 
 ϋʔυ΢ΣΞ ػثΛ෺ཧతʹग़ՙ ނোͤͣʹ௕࣋ͪͤ͞ Δ ʢ଱ٱੑʣΞΠςϜ͕༩͑Β Εͨ৚݅ͷԼͰɺ༩͑ΒΕͨ ظؒɺނোͤͣʹɺཁٻͲ͓ Γʹ਱ߦͰ͖Δೳྗ 1960~ 80 ιϑτ΢Σ Ξ ιʔείʔυɾ࣮ߦϑΝΠ ϧɺ·ͨ͸ɺΠϯετʔϧ͞ Εͨίϯϐϡʔλ͝ͱೲ඼ ίϯϙʔωϯτͱͦͷ ૊Έ߹Θͤͷग़ྗͷͦ ΕͧΕΛࣄલʹ֬ೝ ʢอશੑʣʢઃܭ৴པ ੑʣ 1980~ 2000 Πϯλʔ ωοτ ୯ҰͷڊେωοτϫʔΫΛڞ ༗ͯ͠ར༻ ߴ଎௨৴ɺ஗Ԇɾ఻ૹ ޡΓɺϊʔυނোΛલ ఏͱ͢Δ௨৴ϓϩτί ϧͷઃܭ ʢ૯߹৴པੑʣΞΠςϜ ͕ɼཁٻ͞Εͨͱ͖ʹɺͦ ͷཁٻͲ͓Γʹɺ਱ߦ͢Δ ͨΊͷೳྗ 2000 ~ Ϋϥ΢υ ࣄۀऀʹΑΓγεςϜΛूத ؅ཧɾৗ࣌Քಇɻར༻ऀ͸Π ϯλʔωοτܦ༝Ͱར༻ ৴པੑͷ௿͍ίϯϙʔ ωϯτ܈Λ౔୆ʹ৴པ ੑͷߴ͍γεςϜઃܭ ௥Ճͷݫີͳఆٛ͸֬ೝͰ ͖ͣɻΑΓར༻ऀ໨ઢͷ৴ པੑΛݸผ۩ମతʹఆٛɻ ※1 JIS Z 8115:2019 ※1 ※1 [Saleh+, 2006] Highlights from the early (and pre-) history of reliability engineering 
 [Kleppmann,2017] Designing Data-intensive Applications: The big ideas behind reliable, scalable, and maintainable systems ※1 [ࢁຊ+ 2021] ֬཰ɾ౷ܭ͔Β࢝ΊΔ ΤϯδχΞͷͨΊͷ৴པੑ޻ֶ- ਎ۙͳނো͔ΒӉ஦։ൃ·Ͱ -,ίϩφࣾ

9 ৴པੑʹؔΘΔ΋ͷ͝ͱͷྺ࢙తมભ ೥୅ ৴པੑͷର৅ γεςϜͷఏڙܗଶ ߴ৴པԽͷߟ͑ํ ৴པੑͷఆٛ 1940~ 60 
 ϋʔυ΢ΣΞ ػثΛ෺ཧతʹग़ՙ ނোͤͣʹ௕࣋ͪͤ͞ Δ ʢ଱ٱੑʣΞΠςϜ͕༩͑Β Εͨ৚݅ͷԼͰɺ༩͑ΒΕͨ ظؒɺނোͤͣʹɺཁٻͲ͓ Γʹ਱ߦͰ͖Δೳྗ 1960~ 80 ιϑτ΢Σ Ξ ιʔείʔυɾ࣮ߦϑΝΠ ϧɺ·ͨ͸ɺΠϯετʔϧ͞ Εͨίϯϐϡʔλ͝ͱೲ඼ ίϯϙʔωϯτͱͦͷ ૊Έ߹Θͤͷग़ྗͷͦ ΕͧΕΛࣄલʹ֬ೝ ʢอશੑʣʢઃܭ৴པ ੑʣ 1980~ 2000 Πϯλʔ ωοτ ୯ҰͷڊେωοτϫʔΫΛڞ ༗ͯ͠ར༻ ߴ଎௨৴ɺ஗Ԇɾ఻ૹ ޡΓɺϊʔυނোΛલ ఏͱ͢Δ௨৴ϓϩτί ϧͷઃܭ ʢ૯߹৴པੑʣΞΠςϜ ͕ɼཁٻ͞Εͨͱ͖ʹɺͦ ͷཁٻͲ͓Γʹɺ਱ߦ͢Δ ͨΊͷೳྗ 2000 ~ Ϋϥ΢υ ࣄۀऀʹΑΓγεςϜΛूத ؅ཧɾৗ࣌Քಇɻར༻ऀ͸Π ϯλʔωοτܦ༝Ͱར༻ ৴པੑͷ௿͍ίϯϙʔ ωϯτ܈Λ౔୆ʹ৴པ ੑͷߴ͍γεςϜઃܭ ௥Ճͷݫີͳఆٛ͸֬ೝͰ ͖ͣɻΑΓར༻ऀ໨ઢͷ৴ པੑΛݸผ۩ମతʹఆٛɻ ※1 JIS Z 8115:2019 ※1 ※1 [Saleh+, 2006] Highlights from the early (and pre-) history of reliability engineering 
 [Kleppmann,2017] Designing Data-intensive Applications: The big ideas behind reliable, scalable, and maintainable systems ※1 [ࢁຊ+ 2021] ֬཰ɾ౷ܭ͔Β࢝ΊΔ ΤϯδχΞͷͨΊͷ৴པੑ޻ֶ- ਎ۙͳނো͔ΒӉ஦։ൃ·Ͱ -,ίϩφࣾ ෳ੡඼͔Βৗ࣌ՔಇͷҰ఺΋ͷ΁

10 ৴པੑʹؔΘΔ΋ͷ͝ͱͷྺ࢙తมભ ೥୅ ৴པੑͷର৅ γεςϜͷఏڙܗଶ ߴ৴པԽͷߟ͑ํ ৴པੑͷఆٛ 1940~ 60 
 ϋʔυ΢ΣΞ ػثΛ෺ཧతʹग़ՙ ނোͤͣʹ௕࣋ͪͤ͞ Δ ʢ଱ٱੑʣΞΠςϜ͕༩͑Β Εͨ৚݅ͷԼͰɺ༩͑ΒΕͨ ظؒɺނোͤͣʹɺཁٻͲ͓ Γʹ਱ߦͰ͖Δೳྗ 1960~ 80 ιϑτ΢Σ Ξ ιʔείʔυɾ࣮ߦϑΝΠ ϧɺ·ͨ͸ɺΠϯετʔϧ͞ Εͨίϯϐϡʔλ͝ͱೲ඼ ίϯϙʔωϯτͱͦͷ ૊Έ߹Θͤͷग़ྗͷͦ ΕͧΕΛࣄલʹ֬ೝ ʢอશੑʣʢઃܭ৴པ ੑʣ 1980~ 2000 Πϯλʔ ωοτ ୯ҰͷڊେωοτϫʔΫΛڞ ༗ͯ͠ར༻ ߴ଎௨৴ɺ஗Ԇɾ఻ૹ ޡΓɺϊʔυނোΛલ ఏͱ͢Δ௨৴ϓϩτί ϧͷઃܭ ʢ૯߹৴པੑʣΞΠςϜ ͕ɼཁٻ͞Εͨͱ͖ʹɺͦ ͷཁٻͲ͓Γʹɺ਱ߦ͢Δ ͨΊͷೳྗ 2000 ~ Ϋϥ΢υ ࣄۀऀʹΑΓγεςϜΛूத ؅ཧɾৗ࣌Քಇɻར༻ऀ͸Π ϯλʔωοτܦ༝Ͱར༻ ৴པੑͷ௿͍ίϯϙʔ ωϯτ܈Λ౔୆ʹ৴པ ੑͷߴ͍γεςϜઃܭ ௥Ճͷݫີͳఆٛ͸֬ೝͰ ͖ͣɻΑΓར༻ऀ໨ઢͷ৴ པੑΛݸผ۩ମతʹఆٛɻ ※1 JIS Z 8115:2019 ※1 ※1 [Saleh+, 2006] Highlights from the early (and pre-) history of reliability engineering 
 [Kleppmann,2017] Designing Data-intensive Applications: The big ideas behind reliable, scalable, and maintainable systems ※1 [ࢁຊ+ 2021] ֬཰ɾ౷ܭ͔Β࢝ΊΔ ΤϯδχΞͷͨΊͷ৴པੑ޻ֶ- ਎ۙͳނো͔ΒӉ஦։ൃ·Ͱ -,ίϩφࣾ ෦඼͕ؒ૬ޓ࡞༻͢ΔΑ͏ͳγεςϜ΁ ෦඼ͷނোΛલఏͱͨ͠ઃܭͱอक΁

11 ৴པੑʹؔΘΔ΋ͷ͝ͱͷྺ࢙తมભ ೥୅ ৴པੑͷର৅ γεςϜͷఏڙܗଶ ߴ৴པԽͷߟ͑ํ ৴པੑͷఆٛ 1940~ 60 
 ϋʔυ΢ΣΞ ػثΛ෺ཧతʹग़ՙ ނোͤͣʹ௕࣋ͪͤ͞ Δ ʢ଱ٱੑʣΞΠςϜ͕༩͑Β Εͨ৚݅ͷԼͰɺ༩͑ΒΕͨ ظؒɺނোͤͣʹɺཁٻͲ͓ Γʹ਱ߦͰ͖Δೳྗ 1960~ 80 ιϑτ΢Σ Ξ ιʔείʔυɾ࣮ߦϑΝΠ ϧɺ·ͨ͸ɺΠϯετʔϧ͞ Εͨίϯϐϡʔλ͝ͱೲ඼ ίϯϙʔωϯτͱͦͷ ૊Έ߹Θͤͷग़ྗͷͦ ΕͧΕΛࣄલʹ֬ೝ ʢอશੑʣʢઃܭ৴པ ੑʣ 1980~ 2000 Πϯλʔ ωοτ ୯ҰͷڊେωοτϫʔΫΛڞ ༗ͯ͠ར༻ ߴ଎௨৴ɺ஗Ԇɾ఻ૹ ޡΓɺϊʔυނোΛલ ఏͱ͢Δ௨৴ϓϩτί ϧͷઃܭ ʢ૯߹৴པੑʣΞΠςϜ ͕ɼཁٻ͞Εͨͱ͖ʹɺͦ ͷཁٻͲ͓Γʹɺ਱ߦ͢Δ ͨΊͷೳྗ 2000 ~ Ϋϥ΢υ ࣄۀऀʹΑΓγεςϜΛूத ؅ཧɾৗ࣌Քಇɻར༻ऀ͸Π ϯλʔωοτܦ༝Ͱར༻ ৴པੑͷ௿͍ίϯϙʔ ωϯτ܈Λ౔୆ʹ৴པ ੑͷߴ͍γεςϜઃܭ ௥Ճͷݫີͳఆٛ͸֬ೝͰ ͖ͣɻΑΓར༻ऀ໨ઢͷ৴ པੑΛݸผ۩ମతʹఆٛɻ ※1 JIS Z 8115:2019 ※1 ※1 [Saleh+, 2006] Highlights from the early (and pre-) history of reliability engineering 
 [Kleppmann,2017] Designing Data-intensive Applications: The big ideas behind reliable, scalable, and maintainable systems ※1 [ࢁຊ+ 2021] ֬཰ɾ౷ܭ͔Β࢝ΊΔ ΤϯδχΞͷͨΊͷ৴པੑ޻ֶ- ਎ۙͳނো͔ΒӉ஦։ൃ·Ͱ -,ίϩφࣾ ෦඼ͷނোΛલఏͱͨ͠ΑΓ޿ൣғͷఆٛ΁

12 ։ൃ͞Εͨ੒Ռ෺Λೲ඼ͨ͠ͷͪʹɺӡ༻ɾอक͢Δ 2ஈ֊ͷϥΠϑαΠΫϧ ιϑτ΢ΣΞγεςϜͷϥΠϑαΠΫϧͷมભ Ϋϥ΢υ্Ͱৗ࣌Քಇ͢ΔγεςϜͷ։ൃͱӡ༻Λಉ࣌ʹ࣮ફ͢Δ ஈ֊෼͚ͳ͠ͷϥΠϑαΠΫϧ աڈ ݱ୅ ࢀߟɿDevOps [Allspaw+, 2009] 10+ Deploys Per Day: Dev and Ops Cooperation at Flickr https://www.slideshare.net/jallspaw/10-deploys-per-day-dev-and- ops-cooperation-at-flickr ఏڙܗଶ͕ৗ࣌Քಇ͢ΔҰ఺΋ͷαʔϏε΁มԽ ҰํͰɺมߋස౓͕ߴ͍͜ͱ͔Βɺมߋ͕ো֐ͷҾ͖ۚͱͳΔ [Beyer+, 2018] The Site Reliability Workbook: Practical Ways to Implement SRE GoogleͰ͸ো֐ͷҾ͖ۚͷ͏ͪ68%͸มߋʹΑΔ΋ͷ [Allspaw+, 2009] [Beyer+, 2018]

13 มߋʹΑΔো֐ൃੜΛલఏͱ͢Δߟ͑ํ ɾ׬શͳ৴པੑ͸໨ࢦ͞ͳ͍ ɾ৴པੑͷࢦඪͱͦͷ໨ඪ஋Λઃఆ ɾ໨ඪ஋ΛԼݶͱͯ͠ɺ։ൃऀ͸ੵۃతʹมߋՄೳͱ͢Δ ɾʮ৴པੑʯΛ੍ޚ͠ɺมߋ଎౓Λ࠷େԽ͢Δ Site Reliability Engineering (SRE) ཱ྆͢Δʹ͸ ߴස౓ͷมߋ ߴ৴པੑ [Beyer+, 2016] Site Reliability Engineering: How Google Runs Production Systems [Beyer+, 2016]

14 ɾιϑτ΢ΣΞʹΑΔӡ༻ࣗಈԽ͸͢Ͱʹ࣮ફ͞Ε͍ͯͨ ɾมߋΛલఏͱͨ͠ো֐Λڐ༰͢ΔͨΊͷΞϓϩʔν͕ීٴ Googleʹ͓͚Δᴈ໌ظ ੈքతͳ෩Ӣظ ɾӡ༻Λιϑτ΢ΣΞΤϯδχΞϦϯάͰ࠶ఆٛ ɾιϑτ΢ΣΞʹΑΔΦϖϨʔγϣϯͷࣗಈԽ Site Reliability EngineeringʢSREʣͷීٴ 2004೥ 2014೥ 
 Ҏ߱ ίϛϡχςΟͰڞ༗͞ΕΔSREͷݫ֨ͳఆٛ͸·ͩͳ͍ 2014೥ USENIXͰSREconͷॳ։࠵ ”৴པੑ޻ֶ”ͷΑ͏ͳֶज़ྖҬͱͯ͠ͷ੒ख़౓߹͍͸·ͩઙ͍ طଘͷߴ৴པԽख๏ͱͷؔ܎ੑΛߟ͑Δ

15 1. ϓϩτίϧʹ 
 جͮࣗ͘ಈ੍ޚ 2. ؅ཧऀʹΑΓએݴ͞Εͨ๬·͠ ͍ঢ়ଶʹ௥ै͢Δࣗಈ੍ޚ 3. ΦϖϨʔλʔʹΑΔखಈ੍ޚ Ϋϥ΢υͷ଱ো֐ੑͷͨΊͷ֊૚Ϟσϧʢಠࣗʣ • ίϯϙʔωϯτ΍௨৴ϨϕϧͰͷ 
 ނো΍ྼԽରԠ • ఻ૹ੍ޚɾܦ࿏੍ޚϓϩτίϧɺ 
 ෼ࢄ߹ҙΞϧΰϦζϜͳͲ • ܭࢉػϦιʔεͷ 
 ల։ɾࣗಈ৳ॖɾ؅ཧ • Borg, KubernetesͳͲͷ 
 ΦʔέετϨʔλʔ • ৴པੑͷ໨ඪ஋Λຬͨ͢Α͏ʹ 
 ো֐ʹରͯ͠खಈͰରԠ • ༧๷ɾ༧ଌɾݕ஌ɾݪҼ਍அɾ 
 ؇࿨ɾࣄޙ෼ੳɾम෮ ʢϑΥʔϧττϨϥϯεʣ ࡢࠓͷٕज़Λ౿·͑ͨ3૚ΦχΦϯϞσϧɻSREͰ͸3ͷ૚ʹϑΥʔΧε Service-level Component-level System-level

16 ΦϖϨʔλʔʹΑΔखಈ੍ޚʹཁ͢Δ࣌ؒ ※1 The VOID Report 2021 https://www.thevoid.community/report 599૊৫ͷ1,818ͷো֐ϨϙʔτʹΑΔͱɺো֐ͷ൒਺Ҏ্͸2࣌ؒҎ಺ʹղܾ ※1 ճ෮Λ୹ॖ͢Δ ༨஍͸े෼ʹ͋Δ

17 2. ؅ཧऀʹΑΓએݴ͞Εͨ๬·͠ ͍ঢ়ଶʹ௥ै͢Δࣗಈ੍ޚ 3. ΦϖϨʔλʔʹΑΔखಈ੍ޚ Ϋϥ΢υͷ଱ো֐ੑͷͨΊͷ֊૚Ϟσϧʢಠࣗʣ AIʹΑΔࣗಈԽʢAIOpsʣ • ίϯϙʔωϯτ΍௨৴ϨϕϧͰͷ 
 ނো΍ྼԽରԠ • ఻ૹ੍ޚɾܦ࿏੍ޚϓϩτίϧɺ 
 ෼ࢄ߹ҙΞϧΰϦζϜͳͲ • ܭࢉػϦιʔεͷ 
 ల։ɾࣗಈ৳ॖɾ؅ཧ • Borg, KubernetesͳͲͷ 
 ΦʔέετϨʔλʔ • ৴པੑͷ໨ඪ஋Λຬͨ͢Α͏ʹ 
 ো֐ʹରͯ͠खಈͰରԠ • ༧๷ɾ༧ଌɾݕ஌ɾݪҼ਍அɾ 
 ؇࿨ɾࣄޙ෼ੳɾम෮ ো֐ͷػߏͷ೺Ѳ͕೉͍ͨ͠Ίɺσʔλۦಈͷ ֶशʹΑΔࣗಈԽ͕ݚڀ͞Ε͍ͯΔ 1. ϓϩτίϧʹ 
 جͮࣗ͘ಈ੍ޚ ʢϑΥʔϧττϨϥϯεʣ

18 ɾITΦϖϨʔλ͸खಈͰ໘౗ͳ؅ཧ࡞ۀ΍ೝ஌ෛՙͷߴ͍࡞ۀ͕ཁٻ͞ΕΔ ɾো֐ͷݕ஌΍ݪҼͷ਍அ ɾෛՙʹԠͨ͡εέʔϧΞ΢τɾεέʔϧΠϯ ɾΞϥʔςΟϯάͷ؅ཧɺΠϯγσϯτରԠ AIOps (Artificial Intelligence for IT Operations) [Notaro ’20]: Notaro, P, Jorge C, and Michael G. "A Systematic Mapping Study in AIOps.” ICSOC. Springer, Cham, 2020. [Dang’19]: Dang, Y, Qingwei L, and Peng H. "AIOps: Real-World Challenges and Research Innovations." ICSE-Companion. IEEE, 2019. ɾGartnerʹΑΓ2017೥ʹఏএ͞Εͨ ʢAlgorithmic IT Operationsઆ΋͋Δʣ ※1 https://blogs.gartner.com/andrew-lerner/2017/08/09/aiops-platforms/ ※1 ɾITαʔϏεͷ؅ཧͱվળʹɺ౷ܭղੳ΍ػցֶशΛ͸͡Ίͱ͢ΔAIʢਓ޻஌ ೳʣٕज़Λద༻͢ΔऔΓ૊Έͷ૯শ

19 SREͱAIOpsͷؔ܎ ɾ׬શͳ৴པੑΛ໨ࢦ͞ͳ͍ϙϦγʔʹ͸ɺσʔλۦಈܕͷAI͕΋ͭ ՄṩੑΛ৫ΓࠐΈ΍͍͢ ɾ׬શͳ৴པੑΛ໨ࢦ͢৔߹ɺϒϥοΫϘοΫεͰ͋ΔAIΛ৴͡ΒΕͳ͍

20 ɾ1980೥୅ޙ൒ʹ͸ɺωοτϫʔΫ؅ཧʹɺ஌ࣝϕʔεAI΍χϡʔϥϧωο τϕʔεAIΛԠ༻͢ΔՄೳੑ͕ٞ࿦͞Ε͍ͯΔ ৘ใγεςϜͷӡ༻ʹAIΛԠ༻͢ΔىݯΛ୳Δ [Cebulka 1989]: Cebulka KD, et al., Applications of arti fi cial intelligence for meeting network management challenges in the 1990s, IEEE GLOBECOM 1989. ɾಛఆͷαʔϏεΛαϙʔτ͢ΔͨΊͷωοτϫʔΫͷॳظઃܭ ɾηϯτϥϧΦϑΟεؒͷઓज़తͳઃඋܭը ɾεΠον͔Βͷϝοηʔδͷ؂ࢹͱ਍அ [Notaro 2021]: Notaro P, et al., A Survey of AIOps Methods for Failure Management. ACM TIST, 2021. ɾ1990೥୅ॳ಄͔ΒΦϯϥΠϯͷιϑτ΢ΣΞ΍ϋʔυ΢ΣΞͷނো༧஌ Ϟσϧ͕͍͔ͭ͘ఏҊ͞Ε͍ͯΔɽͦͷଞͷނো๷ࢭํ๏ͳͲ΋ಉ࣌ظ [Cebulka 1989] [Notaro 2021]

21 ݱ୅ʹ͓͚ΔAIOpsͷߩݙྖҬ [Notaro ’20]: Notaro, P, Jorge C, and Michael G. "A Systematic Mapping Study in AIOps.” ICSOC. Springer, Cham, 2020. [Notaro ’20]: Fig.2 Taxonomy of AIOps as observed in the identified contributions 
 ΑΓసࡌ ো֐؅ཧʹؔ͢Δݚڀ Ϧιʔεͷׂ౰ͳͲͷ 
 ࠷దԽʹؔ͢Δݚڀ

22 AIOpsͷݚڀྖҬ͝ͱͷ࿦จ਺ [Notaro+, ICSOC2020] Notaro, P, Jorge C, and Michael G. "A Systematic Mapping Study in AIOps ɾAIOpsؔ࿈ͷ࿦จ਺ɿ670ʢ2020೥࣌఺ʣ ɾ670݅ͷ62.1%͕Failure Managementʢো֐؅ཧʣʹؔ࿈͍ͯ͠Δ ɾো֐༧ଌʢ26.4ˋʣো֐ݕग़ʢ33.7ˋʣݪҼ෼ੳʢ26.7ˋʣ ࿦จ਺͸૿Ճ܏޲ [Notaro+, ICSOC2020]

23 AIOpsʹ͓͚Δো֐؅ཧͷݚڀ ༧ଌ 
 ༧๷ ݪҼ਍அ ؇࿨ ࣄޙ෼ੳ ݕ஌ म෮ ͍ͣΕ΋ΦϖϨʔλʔͷ ܦݧ΍௚ײʹґଘ͢ΔλεΫ ݚڀ࿦จ͕ଟ͍λεΫ ௚઀తͳ൑அ΍ૢ࡞ΑΓ͸ ิॿతͳ৘ใࢧԉͷͨΊͷݚڀ͕ ࢧ഑త [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 ݹయతػցֶश ਂ૚ֶश CNN/RNN /LSTM/GNN… ౷ܭతҼՌਪ࿦ ౷ܭతػցֶश ϝτϦΫε/ϩά/τϨʔε/Πϕϯτ/Ξ ϥʔτͳͲͷӡ༻σʔλΛಛ௃ྔͱ͢Δ

24 ΑΓৄࡉͳAIOpsͷݚڀࣄྫ https://speakerdeck.com/yuukit/sre-next-2022 AIOpsݚڀ࿥ʕSREͷͨΊͷγεςϜো֐ͷࣗಈݪҼ਍அ SRE NEXT 2022

25 ɾ ϋʔυ΢ΣΞ͔Βιϑτ΢ΣΞɺΫϥ΢υ΁ͱ৘ใγεςϜͷܗଶ ͕มભ͢ΔʹͭΕͯɺ৴པੑͷΞϓϩʔν͕αʔϏεࢦ޲΁มભɻ ɾSRE͸ɺΦϖϨʔγϣϯΛࣗಈԽ্ͨ͠Ͱɺ৴པੑࢦඪʹԼݶΛઃ ఆ͠ɺมߋ଎౓ΛߴΊΔɺো֐ڐ༰ΞϓϩʔνͰ͋Δɻ ɾ ଱ো֐ੑͷͨΊͷ֊૚Ϟσϧͷ͏ͪɺ࠷֎֪ͷखಈ੍ޚʹରͯ͠ɺ AIʹΑΔࣗಈԽʢAIOpsʣ͕ݚڀ͞Ε͍ͯΔɻ ɾSREͷݪଇʹ͸ɺAI͕΋ͭՄṩੑΛ৫ΓࠐΈ΍͍͢ͱظ଴͢Δ ɾݱࡏ͸ิॿతͳ৘ใࢧԉʹཹ·Δ ·ͱΊɿ1. Ϋϥ΢υʹ͓͚Δ৴པੑΤϯδχΞϦϯά

1. Ϋϥ΢υʹ͓͚Δ৴པੑΤϯδχΞϦϯά 2. AI࣌୅ʹ͓͚Δ৴པੑΤϯδχΞϦϯάͷະདྷ 3. AIͱͷڠಇʹΑΔ৴པੑΤϯδχΞϦϯάͷݕ౼ 4. ͓ΘΓʹ 26 ΞδΣϯμ ݱࡏɺͲ͏ͳͬͯ 
 ͍Δͷ͔ 20೥ઌͷະདྷͰ 
 Ͳ͏͋Γ͍͔ͨ ະདྷͱݱࡏͷࠩΛ 
 ຒΊΔಓے͸ͳʹ͔

27 དྷͨΔ΂͖AI࣌୅΁޲͚ͯະདྷΛߟ͑Δ ɾ৴པੑ͚ͩΛऔΓ্͛ͯɺະདྷΛޠΔͷ͸೉͍͠ ɾ৴པੑΛཁٻ͢ΔਓʑͱΞϓϦέʔγϣϯͷ͋Γํ͔Βߟ͑Δ ະདྷͷ͋Δ࣌఺ͷئ๬ ͔Β࢝ΊΔ 2040s 2022(ݱࡏ) 2045 ٕज़త 
 ಛҟ఺ όοΫΩϟεςΟϯάͰ ະདྷ͔Βݱࡏ΁Ḫߦ ϑΣʔζ2 ϑΣʔζ1 ϑΣʔζ3 2045೥ͷγϯΪϡϥ ϦςΟൃੜΛԾఆ

AIʹਓؒͷ࢓ࣄ͕ୣΘΕΔ͚ͩͷ ະདྷ؍͸͓΋͠Ζ͘ͳ͍ ਓؒಉ࢜ͷ૬ޓཧղ͕೉͍͜͠ͱ͔Β AI͕ਓؒͷજࡏతࢥߟΛཧղ͢Δ͜ͱ΋༰қͰ͸ͳ͍͸ͣ


 ສਓ͕ࣗΒʹ࠷దԽ͞ΕͨΞϓϦέʔγϣϯΛ 
 AIͱͷର࿩Λ௨ͯࣗ͡༝ʹ੡࡞Մೳͳ࣌୅ 2040೥୅ ηϧϑΫϥϑτʢSelf Craftingʣ AIʹΑΔࣗಈԽΛಥ͖٧ΊΔͱɺٯઆతʹਓؒ͸૑଄తʹͳΔ

30 ݱࡏʢ2022೥ʣͷΞϓϦέʔγϣϯ։ൃ Ϋϥ΢υ Πϯλʔωοτ ඪ४Խ͞Εͨ 
 ػೳͱ 
 ΠϯλʔϑΣΠε ։ൃऀ ඪ४Խࢦ޲ͷੈք ཁૉٕज़ ΞϓϦέʔγϣϯ։ൃऀ͸Ϋϥ΢υ্ͷσʔλߏ଄΍ ܭࢉϢχοτ΋ඪ४Խ͞Εͨ΋ͷΛར༻ ΞϓϦέʔγϣϯ 
 ͷܧଓߋ৽ ඪ४Խ͞Εͨ 
 ௨৴ϓϩτίϧͱAPI αʔϏε ଟ਺ͷར༻ऀʹڞ௨ʹΈΒΕΔજࡏతͳχʔζΛൃݟ ͠ɺඪ४Խ͞ΕͨػೳͱΠϯλʔϑΣΠεΛఏڙ ඪ४Խ͞Εͨ 
 σʔλߏ଄ ඪ४Խ͞Εͨ 
 ܭࢉϢχοτ

31 ະདྷʢ2040sʣͷΞϓϦέʔγϣϯ։ൃΫϥϑτ ݸผԽࢦ޲ͷੈք ۭؒͱͷΠϯλϥΫγϣϯ ʢXRʣ Πϯλʔωοτ Ϋϥ΢υ ֶशܕ௨৴ 
 ϓϩτίϧ [Kraska+, SIGMOD2018] The Case for Learned Index Structures ֶशܕ σʔλߏ଄ ηϧϑ Ϋϥϑτ AI AI AI αʔϏε ར༻ऀͷજࡏతͳχʔζʹࢸΔ· ͰɺAIͱར༻ऀ͕ػೳͱΠϯλʔ ϑΣΠεΛର࿩తɾମݧతʹ࣮૷ ཁૉٕज़ ΞϓϦέʔγϣϯͷཁٻ 
 ʹ͋Θֶͤͨशܕͷݸผ 
 ࠷దԽ ૬ޓ࡞༻ʹ ΑΔਐԽ [Ma+, EuroSys2022] Multi-Objective Congestion Control [Kraska+, SIGMOD2018] [Ma+, EuroSys2022] e2eͰར༻ऀͷཁٻʹԠͯ͡ ࠷దͳϓϩάϥϜͱϓϩτί ϧ͕ಈత͔ͭదԠతʹมԽ

32 ηϧϑΫϥϑτͷੈքʹ͓͚Δ৴པੑ ༗ݶͷڞ༗ࢿݯ AI AI AI Πϯλʔωοτ Ϋϥ΢υ ࢿݯͷཁٻ ద੾ͳ 
 ৴པੑ໨ඪΛ 
 ܾఆ͢Δඞཁ͕ 
 ͋Δ ಛఆͷηϧϑΫϥϑτΞϓϦͷ৴པੑ໨ඪΛ100%ʹ͚ۙͮΔ΄Ͳ… ࢿݯফඅˢ: ৑௕ੑ΍Ԡ౴଎౓ΛߴΊΔ΄Ͳɺଟ͘ͷࢿݯΛফඅɻଞऀͷຬ଍ ౓ΛԼ͛ΔՄೳੑ༗Γɻ มߋ଎౓ˣ: ηϧϑΫϥϑτʹΑΓมߋ͢Δ΄Ͳɺมߋޙͷӡ༻σʔλ͕଍Γ ͳ͘ͳΓɺো֐ͷ༧ଌɾ༧๷ਫ਼౓ͳͲ͕௿Լ [Mogul+, HotOS2019] Nines are not enough: Meaningful metrics for clouds [Mogul+, HotOS2019]

AI͸ ۉߧ఺ͱͯ͠ͷ৴པੑ໨ඪΛ ద੾ʹܾఆՄೳ͔ʁ

34 ਓ͕ؒAIʹ໋ྩʢએݴʣ͢Δ͜ͱͷݶք [BEATLESS]: ௕୩ හ࢘, B E A T L E S S, ݄ץχϡʔλΠϓ, ֯઒ॻళ, 2012೥. ʪ໋ྩ͞ΕΔਓ޻஌ೳͷଆʹཱͬͯɺߟ͑ͯΈ͍ͯͩ͘͞ɻ໋ྩ ͸ɺᐆດͳᶸҙຯᶹͷ૊Έ߹Θͤͱͯ͠༩͑ΒΕɺͦͷᶸҙຯᶹղ ऍ΋·ͨɺ͢΂໋ͯྩΛ༩͑ΔਓؒʹѲΒΕ͍ͯ·͢ɻʜਓ޻஌ೳ ͸ɺͲ͜·Ͱ໋ྩऀͷݴ͏ʰద੾ͳʱղ౴Λग़ͤΔͷͰ͔͢ʁʫ ௒ߴ౓AI ʬώΪϯζʭ [BEATLESS] PHASE13ʮBEATLESSʯΑΓ 
 Ұ෦จࣈ৭Λมߋͯ͠Ҿ༻ ʪʜ͔ͩΒɺࢲʹ͸ʰ৴͡Δʱ͜ͱ͸Ͱ͖·ͤΜɻͦ͏͍͏ಓ۩ͷ ڍಈΛਖ਼֬ʹίϯτϩʔϧ͍ͨ͠ͷͰ͋Ε͹ɺᐆດ͞ͷͳ͍൑அج४ Λ͍ͩ͘͞ʫ ௒ߴ౓AI ʬώΪϯζʭ [BEATLESS] LAST PHASE ʮIMAGE AND LIFEʯΑΓ 
 Ұ෦จࣈ৭Λมߋͯ͠Ҿ༻ ʮΘͨ͠͸ɺΦʔφʔͰ͋ΔΞϥτ͞ΜͷͨΊʹࢿݯΛ഑෼͢ΔίϯτϩʔϥʔͰ͢ɻɹɹ ᶸະདྷΛσβΠϯᶹͯ͠ཉ͍͠ͱ͸ɺ഑෼ͷͨΊͷج४఺Λઃఆͯ͠ཉ͍͠ͱ͍͏͜ͱͰ͢ʯ hIEʬϨΠγΞʭ[BEATLESS] PHASE10ʮPLUS ONEʯΑΓҰ෦จࣈ৭Λมߋͯ͠Ҿ༻

35 ʰ2001 ೥Ӊ஦ͷཱྀʱHAL 9000 18ষ SREͷͨΊͷػցֶशೖ໳ ͔ΒͷҾ༻ ” ͨͬͨࠓɺAE35Ϣχοτͷো֐Λݕग़͠·ͨ͠ɻ ࢲ͸72࣌ؒҎ಺ʹ100%ͷ֬཰Ͱػೳఀࢭ͠·͢ɻ” ― HAL 9000ɺʰ2001 ೥Ӊ஦ͷཱྀʱ “͜ͷөը͕ඳ͘ະདྷΛઌݟͷ໌Λ΋ͬͯߏ૝ͨ͠ͷ͸Ξʔ αʔɾCɾΫϥʔΫ(Arthur C. Clarke)ͰɺγεςϜͱϋʔυ΢Σ Ξͷো֐ൃੜΛԿ࣌ؒ΋લʹ༧ଌͰ͖Δ׬શࣗಈԽαʔϏεͱ AI Λ૊Έ߹Θͤ·ͨ͠ɻHAL 9000 ͸ɺཱࣗͨࣗ͠ݾௐ੔ܕͷ ܽ఺͕ͳ͍ػցͱ͍͏ਓྨͷເ(͋Δ͍͸ѱເ)Ͱ͋Γɺਓؒʹ Αͬͯఆٛ͞Εͨ໨ඪΛୡ੒͢ΔͨΊʹɺӉ஦ધͷ৐һͱϛο γϣϯͷ྆ํʹไ࢓͠·͢ɻ” David N. Blank-Edelmanɹฤɺࢁޱ ೳ᫫ɹ؂༁ɺ౉ᬒ ྃհɹ༁, SREͷ୳ٻʕʕ༷ʑͳاۀʹ͓͚ΔαΠτϦϥΠΞϏϦςΟΤϯδχΞϦ ϯάͷಋೖͱ࣮ફ, ΦϥΠϦʔɾδϟύϯ, 2021೥.

ར༻ऀͱAI͕ɺར༻ऀʹͱͬͯͷ࠷దͳۉߧ఺Λର࿩తʹ୳Δ ద੾ͳ৴པੑ͕ෆ໌ → ର࿩తΞϓϩʔν ৴པੑɺίετɺมߋ଎౓ͳͲͷ 
 ֤มྔͷ഑෼ͷͨΊͷ࠷దۉߧ఺ ར༻ऀ͕͋Δ΂͖ঢ়ଶΛख़ߟͯ͠એݴͤͣʹɺ 
 ൃݟతʹղΛ୳ࡧՄೳ Ұ୴ղ͕ऩଋͯ͠΋ɺ ঢ়گͷมԽʹԠͯ͡ɺ ࠶౓ର࿩తऩଋΛߦ͏ 36

37 ର࿩తΞϓϩʔνʹΑΔௐ੔ͷྫ Ͱ͖Δ͚ͩམͪͳͯ͘ɺಈ࡞΋ܰ͘͠ ͯ΄͍͠ ※Ի੠΍ςΩετʹΑΔର࿩Ҏ֎ͷ਎ମత ͳૢ࡞ʹΑΔର࿩΋͋Γ͑Δ ৴པੑΛݱ࣮తͳϨϕϧͰߴΊΔͱͳ Δͱɺۚમίετ͸ʓʓԁͰ͢ AI ͍΍͍΍ɺߴ͗͢ΔΑ ˛˛ػೳͷ৴པੑ໨ඪΛ99.999%͔Β 99.9%ʹ௿Լͤ͞Ε͹ɺίετ͸˘˘ ԁ·Ͱ҆͘ͳΓ·͢ AI े෼҆͘ͳ͚ͬͨͲɺ৴པੑ͕མͪΔ ͷ͸ෆ҆ͳΜ͚ͩͲ Ͱ͸ɺࢼ͠ʹɺࠓ͔Β10෼͚ͩ˛˛ ػೳΛྼԽͤ͞ΔͨΊɺ৴པੑʹෆຬ ͕͋Δ͔൑அ͍ͯͩ͘͠͞ AI ΍ͬͺΓ͜Ε͚ͩΤϥʔ͕ͰΔͱෆศ ͩͶ Ͱ͸ɺ৴པੑ໨ඪΛ99.99%ʹͯ͠ɺ ίετ͸✕✕ԁͰ͸Ͳ͏Ͱ͔͢ʁ AI ʮମݧతʯͳ 
 ௐ੔ϓϩηε

ݱ୅ͱະདྷͷ؍఺ผͷൺֱ HCIͷมԽ ಛ௃ ΞϓϦ 
 έʔγϣϯ ཁૉٕज़ ৴པੑ ݱ୅ 
 2022೥ ฏ໘ͷσΟε ϓϨΠΛհ͠ ͨΠϯλϥΫ γϣϯ ඪ४Խࢦ޲ 
 αʔϏεࢤ ޲ ઐ໳ࣄۀऀ͕λʔ ήοτͱͳΔඪ४ తͳར༻ऀΛ૝ఆ ͯ͠ػೳΛ։ൃ ඪ४Խ͞Εͨ σʔλߏ଄ͱ ϓϩτίϧ ར༻ऀͷߦಈʹؔ ͢Δܭଌࢦඪͷ౷ ܭతཁ໿ʹΑΓܾ ఆ ະདྷ 
 2040s Ծ૝ݱ࣮ɾ֦ ுݱ࣮ɾෳ߹ ݱ࣮ʹର͢Δ ޒײΛ௨ͨ͠ ۭؒͱͷΠϯ λϥΫγϣϯ ݸผԽࢦ޲ 
 Ϋϥϑτࢤ ޲ ར༻ऀ͕ࣗ෼ͷᅂ ޷ʹ͋Θͤͨ࠷ద ͳػೳΛࣗΒ੡࡞ AIͱͷର࿩ʹΑΔ ࣗಈϓϩάϥϛϯ ά ΞϓϦʹ͋Θ ֶͤͨशܕͷ σʔλߏ଄ͱ ϓϩτίϧ ৴པੑͱͦͷଞͷ جຊมྔͱͷۉߧ ఺ΛAIͱର࿩త͔ ͭମݧతʹܾఆ 38

39 ɾ2040೥୅ɿݸผԽࢦ޲ΞϓϦέʔγϣϯΛສਓ͕ࣗ෼ͷͨΊʹࣗ෼Ͱ ੡࡞ʢηϧϑΫϥϑτʣ͢Δ࣌୅ʹͳͬͯ΄͍͠ ɾࢿݯ͸༗ݶͰ͋ΔͨΊɺݸਓͷޮ༻ΛແݶʹߴΊΔ͜ͱ͸Ͱ͖ͳ͍ ɾར༻ऀ͕৴པੑͱͦͷଞͷجຊతͳมྔؒͷۉߧ఺Λௐ੔͢Δඞཁ͕ ͋Δ ɾਓ͕ؒAIʹۉߧ఺Λ༧Ί໋ྩʢએݴʣ͓ͯ͘͜͠ͱ͸೉͍͠ ɾద੾ͳ৴པੑΛɺར༻ऀݸผʹɺAIͱର࿩త͔ͭମݧతʹܾఆ͢Δ ·ͱΊɿ2. AI࣌୅ʹ͓͚Δ৴པੑΤϯδχΞϦϯάͷະདྷ

1. Ϋϥ΢υʹ͓͚Δ৴པੑΤϯδχΞϦϯά 2. AI࣌୅ʹ͓͚Δ৴པੑΤϯδχΞϦϯάͷະདྷ 3. AIͱͷڠಇʹΑΔ৴པੑΤϯδχΞϦϯάͷݕ౼ 4. ͓ΘΓʹ 40 ΞδΣϯμ ݱࡏɺͲ͏ͳͬͯ 
 ͍Δͷ͔ 20೥ઌͷະདྷͰ 
 Ͳ͏͋Γ͍͔ͨ ະདྷͱݱࡏͷࠩΛ 
 ຒΊΔಓے͸ͳʹ͔

41 ݱ୅͔Β2040೥୅·Ͱͷ৴པੑΤϯδχΞϦϯά 2040s 2022 2017 2030s 2045 ٕज़త 
 ಛҟ఺ Gartner͕ 
 AIOpsఏএ ٕज़ऀ͕ 
 AIͱڠಇ AIʹΑΔ ো֐ͷࣗ཯ରԠ ٕज़ऀ͕ 
 ৴པੑΛ੍ޚ ৴པੑ໨ඪ͸ ਓ͕ؒએݴɻ AIʹΑΔݕ஌ ΍਍அͷݶఆ తͳิॿ ݱࡏ e2eͰར༻ऀͷཁٻ ʹԠͯ͡ϓϩάϥϜ ͱϓϩτίϧ͕ಈత ͔ͭదԠతʹਐԽ ٕज़ऀ͕γεςϜ ΞʔΩςΫνϟΛઃ ܭ͠ɺAI͕Ϟδϡʔ ϧΛ࣮૷ɾ࿈݁ ٕज़ऀͱAIʹΑΔ ର࿩తͳো֐༧๷ ΍ճ෮ɻ ӡ༻σʔλͷΦʔ ϓϯԽ͕ਐΉ ηϧϑ 
 Ϋϥϑτ ར༻ऀ͕ 
 ৴པੑΛ੍ޚ ϑΣʔζ2 ϑΣʔζ3 ϑΣʔζ̍ ৴པੑ໨ඪ͸αʔ Ϗεࣄۀऀ͕ܾఆ

৴པੑ໨ඪ͸ ਓ͕ؒએݴɻ AIʹΑΔݕ஌ ΍਍அͷݶఆ తͳิॿ ٕज़ऀ͕γεςϜ ΞʔΩςΫνϟΛઃ ܭ͠ɺAI͕Ϟδϡʔ ϧΛ࣮૷ɾ࿈݁ ٕज़ऀͱAIʹΑΔ ର࿩తͳো֐༧๷ ΍ճ෮ɻ ӡ༻σʔλͷΦʔ ϓϯԽ͕ਐΉ 42 ݱ୅͔Β2040೥୅·Ͱͷ৴པੑΤϯδχΞϦϯά 2040s 2022 2017 2030s 2045 ٕज़త 
 ಛҟ఺ Gartner͕ 
 AIOpsఏএ ٕज़ऀ͕ 
 AIͱڠಇ AIʹΑΔ ো֐ͷࣗ཯ରԠ ٕज़ऀ͕ 
 ৴པੑΛ੍ޚ ݱࡏ ηϧϑ 
 Ϋϥϑτ ར༻ऀ͕ 
 ৴པੑΛ੍ޚ ৴པੑ໨ඪ͸αʔ Ϗεࣄۀऀ͕ܾఆ ϑΣʔζ3 ࠓճͷݕ౼ൣғ ϑΣʔζ2 ϑΣʔζ̍ e2eͰར༻ऀͷཁٻ ʹԠͯ͡ϓϩάϥϜ ͱϓϩτίϧ͕ಈత ͔ͭదԠతʹਐԽ

43 ਂ૚ֶश؍఺ͰͷAIOpsͷ໰୊ҙࣝ ݱࡏͷAIOpsͰ͸ɺݸผͷγεςϜ͝ͱʹہॴతʹֶशϞσϧΛ࡞੒ ɾଟ਺ͷγεςϜͷσʔλ͔Βֶश͠ɺେҬతͳֶशϞσϧΛ࡞੒͢Ε͹ɺଞ ෼໺ʢCVɺNLPʣͷΑ͏ͳݦஶͳ੒Ռ͕ಘΒΕΔͷͰ͸ͳ͍͔ ɾ͔͠͠ɺαʔϏεࣄۀऀ͸ɺϓϥΠόγʔอޢͷͨΊɺӡ༻σʔλͷެ։ʹ ੵۃతͰͳ͍ ྫ γεςϜX ίϯϙʔωϯτCX ϝτϦΫεCXM1 ϝτϦΫεCXM2 ଟมྔ࣌ܥྻͷ ֶशϞσϧ ɾ ɾ ɾ

44 ϑΣʔζ1ʢٕज़ऀͱAIͷڠಇʣʹ޲͚ͯͷ՝୊ͱཁ݅ 1. ਖ਼ৗظ͕ؒࢧ഑తͰ͋ΓɺҟৗΛֶश͢ΔͨΊͷσʔλ͕ෆ଍ ↪ ཁ݅ᶃ ނҙʹҟৗΛൃੜͤ͞ɺҟৗΛֶशՄೳ 2. ֶशϞσϧ͕ఏࣔ͢Δ༧ଌͷࠜڌ͕ෆ໌ ↪ ཁ݅ᶄ ༧ଌࠜڌΛΦϖϨʔλʔ͕ཧղՄೳͳݴޠͰఏࣔՄೳ [Soldani+, CSUR2022] Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Survey [Soldani+, CSUR2022] લఏɿগ਺ͷγεςϜͷσʔλ͔ΒͷΈֶश͢Δ ҟৗΛڭ͑Δ ڭΘͬͨ݁ՌΛఏࣔ ڭ͑ͨ͜ͱͷֶ श౓߹͍Λ֬ೝ AI

Interactive AIOps ΦϖϨʔλʔͱAI͕ର࿩తʹର৅γεςϜͷಛ௃Λ 
 ڠಇֶश͢Δίϯηϓτ

46 ཁ݅ᶃɹ࣮ݧՄೳੑʢExperimentabilityʣ AI ऑ఺ͷൃݟͱֶश Chaos Engineering ͔Βண૝ γεςϜతͳऑ఺Λൃݟ͢ΔͨΊ 
 ʹߦ͏࣮ݧͷԁ׈Խ [Rosenthal+, 2020] Chaos Engineering: System Resiliency in Practice [Rosenthal+, 2020] 1. ΦϖϨʔλʔ͸৘ใγεςϜʹނ োΛ஫ೖͨ͠ΓෛՙΛ૿ݮͤ͞Δ 2. ͦͷࡍʹ؍ଌ͞ΕͨσʔλΛAI͕ ֶश͢Δ 3. 1ͱ2ΛҟৗύλʔϯΛม͑ͳ͕Β ܁Γฦ͢ Operator ҟৗΛڭ͑Δ ֓೦ͷ֦ு

47 ཁ݅ᶄɹղऍੑʢExplainabilityʣ ղऍՄೳͳAIʢXAIʣ AI Operator ※̍ https://speakerdeck.com/tsurubee/a-survey-on-interpretable-machine-learning-and-its-application-for-system-operation ※̍ ڭΘͬͨ݁Ռ Λఏࣔ 1. ΦϖϨʔλʔ͸ཁ݅ᶃͰͷҟৗͱ ྨࣅͷҟৗΛ࠶ݱ 2. AI͸ҟৗʹରͯ͠ɺ༧ଌ΍ݪҼΛ ͦͷࠜڌʢد༩ͨ͠ಛ௃ྔʣͱͱ ΋ʹฦ͢ ਓؒʹཧղՄೳͳݴ༿Ͱઆ໌· ͨ͸ఏࣔ͢ΔೳྗΛ΋ͭAI [Adadi+, Access2018] Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) [Adadi+, Access2018] ༧ଌ݁Ռͷ 
 ਖ਼౰Խ ਓؒͱϞσϧؒ Ͱܧଓվળ σόοά ৽ͨͳൃݟ [Adadi+, Access2018] ΑΓFIGURE 5ͷҾ༻

48 ΑΓൃలతͳAIͱͷڠಇͷՄೳੑ γεςϜֶؒशੑ (Intersystem Learnability) ܇࿅Մೳੑ (Trainability) AI AI͕ఏࣔ͢Δ܇࿅ϓϩάϥϜ 
 Λ༻͍ͯΦϖϨʔλʔ͕ো֐ ରԠ܇࿅ ͋ΔγεςϜ͕ 
 ଞγεςϜʢࣗݾͷաڈؚΉʣ ͷֶश಺༰͔Βֶ΂Δ AI AI సҠ AI సҠֶशʹΑΔ - ֶशͷߴ଎Խ - ֎ૠੑΛ֫ಘ Operator ೳಈֶशʹΑΓաڈͷ σʔλͷϥϕϦϯάΛ ܇࿅ϓϩάϥϜʹ૊Έ ࠐΉͱͯ͠ఏࣔ Target Source [Pan+, TKDE2009] A Survey on Transfer Learning. [Pan+, TKDE2009] [Settles,2009] Active Learning Literature Survey [Settles,2009]

49 ɾݱ୅͔Β2040೥୅ʢٕज़ಛҟ఺ؚΉʣ·ͰͷಓےΛ3ͭͷϑΣʔζ ʹ෼཭͠ɺࠓճ͸ɺٕज़ऀ͕AIͱڠಇՄೳͳϨϕϧΛݕ౼͢Δɻ ɾӡ༻σʔλΛ޿͘ೖखͰ͖ͳ੍͍໿ͷൣғͰ͸ɺҟৗͷσʔλΛࣗ Β࡞Γग़ֶ͠श͢Δඞཁ͕͋Δɻ ɾΦϖϨʔλʔͱAI͕ର࿩తʹγεςϜͷಛ௃Λڠಇֶश͢Δίϯη ϓτʮInteractive AIOpsʯΛఏএ͢Δɻ ɾର࿩ͷجຊܕ͸ɺ࣮ݧՄೳੑʢAIʹڭ͑ΔʣͱղऍੑʢAI͔Βઆ ໌ʣͰ͋Δɻ ·ͱΊɿ3. AIͱͷڠಇʹΑΔ৴པੑΤϯδχΞϦϯάͷݕ౼

1. Ϋϥ΢υʹ͓͚Δ৴པੑΤϯδχΞϦϯά 2. AI࣌୅ʹ͓͚Δ৴པੑΤϯδχΞϦϯάͷະདྷ 3. AIͱͷڠಇʹΑΔ৴པੑΤϯδχΞϦϯάͷݕ౼ 4. ͓ΘΓʹ 50 ΞδΣϯμ ݱࡏɺͲ͏ͳͬͯ 
 ͍Δͷ͔ 20೥ઌͷະདྷͰ 
 Ͳ͏͋Γ͍͔ͨ ະདྷͱݱࡏͷࠩΛ 
 ຒΊΔಓے͸ͳʹ͔

51 ຊߨԋશମͷ·ͱΊ ݱࡏ ɾSite Reliability Engineering͸ɺ৴པੑΛ੍ޚର৅ͱ͢Δɻ ɾAIOpsͷݚڀ͕׆ൃͰ͋Γͭͭ΋ɺิॿతͳ৘ใࢧԉʹཹ·Δɻ ະདྷ ɾ2040sɿඪ४Խɾએݴࢦ޲͔ΒݸผԽɾର࿩ࢦ޲ͷ࣌୅΁มભɻ ɾ৴པੑΛݸผͷۉߧ఺΁ɺར༻ऀ͕AIͱͷର࿩త͔ͭମݧతʹऩଋɻ ಓے ɾAIͱٕज़ऀͷڠಇ → AIʹΑΔো֐ͷࣗ཯ରԠ → ར༻ऀ͕৴པੑΛ੍ޚ ɾσʔλ͕ෆ଍͢ΔલఏͰ͸ɺҟৗΛࣗΒ࡞Γग़͍ͯ͘͠ඞཁ͕͋Δɻ ର࿩ͱମݧʢ࣮ݧʣʹΑΔڠಇతͳ৘ใγεςϜͷ੡࡞ͱ੍ޚɻ γεςϜͷجຊཁૉͰ͋Δ৴པੑʹ΋ٴͿɻ

52 AIͷೳྗ͕޲্͢ΔʹͭΕͯɺٕज़ऀʹͱͬͯͷϒϥοΫϘοΫεͷ ൣғ͕େ͖͘ͳΔ ຊߏ૝ͷࠓޙͷݕ౼ࣄ߲ [Bainbridge, Pergamon1983] Ironies of Automation. Analysis, Design and Evaluation of Man–machine Systems Ironies of Automation [Bainbridge, Pergamon1983] ੍ޚγεςϜ͕ߴ౓ʹͳΕ͹ͳΔ΄ͲɺਓؒͷΦϖϨʔλʔͷߩݙ͕ ΑΓॏཁʹͳΔͱ͍͏ൽ೑ Ͳ͜·ͰAIΛ৴͡Δͷ͔ɺAIࣗମͷ৴པੑʹͲ͏Ξϓϩʔν͢Δ͔

53 ओͳࢀߟਤॻ

͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠ ڞಉͰͷٞ࿦ɾݚڀɺ 
 σΟεΧογϣϯɺࢧԉͳͲ 
 Λ͓଴͓ͪͯ͠Γ·͢