さくらインターネット研究所で研究に再挑戦した私の半年間の取り組み

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1. ͘͞ΒΠϯλʔωοτגࣜձࣾ (C) Copyright 1996-2019 SAKURA Internet Inc ͘͞ΒΠϯλʔωοτݚڀॴ ͘͞ΒΠϯλʔωοτݚڀॴͰݚڀʹ࠶௅ઓͨ͠ ࢲͷ൒೥ؒͷऔΓ૊Έ 2020/01/16 ݚڀһ ௽ా തจ ͘͞Βͷ༦΂ ݚڀॴφΠτ
2. 2 1. ࣗݾ঺հ 2. ݚڀॴͰͷ೔ৗ 3. ͜Ε·Ͱͷݚڀͱࠓޙͷల๬ 4. ·ͱΊ ໨࣍
3. 1. ࣗݾ঺հ
4. 4 ࣗݾ঺հ ௽ా തจʢͭΔͨ ͻΖ;Έʣ ɹɹॴଐɹ͘͞ΒΠϯλʔωοτݚڀॴ ݚڀһʢ2019೥8݄ʙʣ ɹɹֶྺɹ۝भେֶେֶӃ ࡐྉ෺ੑ޻ֶઐ߈ म࢜՝ఔमྃ ɹɹɹɹɹ۝भେֶେֶӃ ࡐྉ෺ੑ޻ֶઐ߈ ത࢜՝ఔதୀ ɹɹܦྺɹࡐྉ޻ֶͷݚڀɼফ๷࢜ɼػցֶशΤϯδχΞɼ ɹɹɹɹɹΠϯϑϥΤϯδχΞΛܦͯɼݱ৬ ڵຯྖҬɹػցֶशͱྔࢠίϯϐϡʔλɾΞχʔϦϯά @tsurubee3 https://blog.tsurubee.tech/
5. 5 ܦྺ·ͱΊ ܦྺ ظؒ ࡐྉ޻ֶͷݚڀऀ 4೥ ফ๷࢜ 3೥ ITΤϯδχΞ 3೥ ৘ใ޻ֶͷݚڀऀ 6ϲ݄ ←ΠϚίί
6. 2. ݚڀॴͰͷ೔ৗ
7. • ຖ೔ఆ࣌લ45෼ؒ • ೚ҙࢀՃ • ຊ౰ʹࡶஊ͚ͩͷͱ͖΋͋Ε͹ɼ ݚڀͷٞ࿦Λ͢Δͱ͖΋͋Δ 7 • ֤ݚڀһ͕ڵຯɾؔ৺ʹ೚ͤͯɼ໘ന͍ͱࢥ͏ςʔϚʹͲ͠Ͳ͠औΓ૊ΜͰ͍͘ • ֤ݚڀһ͕ίϯηϓτͰඳ͍ͨੈքΛ࣮ݱ͢Δ্Ͱඞཁͳ՝୊Λࣗ཯తʹݟ͚ͭɼ ݚڀΛਐΊ͍ͯ͘ ݚڀॴͷελΠϧɿࣗ཯ɾ෼ࢄɾڠௐ ࣗ཯ • ݚڀһ͸஍ཧతʹ෼ࢄ͍ͯ͠Δ ౦ژ6໊ʢ٬һݚڀһ2໊ʣɾେࡕ2໊ɾ෱Ԭ2໊ʢ2020೥1݄ݱࡏʣ • िҰճ։࠵ • ࿩͍ͨ͠ਓ͕࿩͢ελΠϧ • ൃදͷࣄલใࠂɾࣄޙใࠂɼ ݚڀͷΞΠσΞͳͲ༷ʑ • ෆఆظ • ݸผʹೋਓͰ࿩͢ͳͲ • ΞΠσΞΛฉ͍ͯ΋ΒͬͨΓɼ ࠷ۙ΍͍ͬͯΔ͜ͱΛ࿩ͨ͠Γ ఆྫձ ࡶஊλΠϜ ͦͷଞ ෼ࢄ ڠௐ
8. 8 ݸਓͷελΠϧ ಇ͖ํ • ࿦จࣥච • ൃදɾσΟεΧογϣϯͷࢿྉ࡞੒ • αʔϕΠɼͳͲ ࿦จࣥචɿ೔ຊޠ1ɼӳޠ1 ࢿྉ࡞੒ɿֶձ1ɼษڧձ3ɼاۀσΟεΧογϣϯ2 ൒೥ؒ ࠓޙ ࣗ෼ͷઐ໳෼໺ͷཱ֬ͱͦͷ෼໺Ͱͷത࢜߸औಘΛ໨ࢦ͢ ۀ຿ • बۀ࣌ؒɿ9:30ʙ18:30ʢ10෼୯ҐͰεϥΠυՄʣ • िʹ2ɼ3ճఔ౓ϦϞʔτϫʔΫʢࡏ୐ۈ຿ɿ8:30ʙ17:30ʣ • ݄ʹ1ɼ2ճఔ౓ग़ு • ࠃ಺ɾࠃࡍֶձʢൃදͷ༗ແʹؔΘΒͣʣ • اۀͱͷσΟεΧογϣϯ • ݚڀॴ߹॓ɼͳͲ
9. 3. ͜Ε·Ͱͷݚڀͱࠓޙͷల๬
10. 10 ݱࡏͷݚڀςʔϚ ҎԼͷ2ͭΛ࣠ʹݚڀ׆ಈΛߦ͍ͬͯΔ ̍ɽϢʔβʹมߋΛཁٻͤͣʹγεςϜมԽʹ௥ैՄೳͳ ɹɹSSHϓϩΩγαʔό ̎ɽྔࢠίϯϐϡʔλɾΞχʔϦϯάϚγϯΛ༻͍ͨݚڀߏ૝
11. 11 ݱࡏͷݚڀςʔϚ ҎԼͷ2ͭΛ࣠ʹݚڀ׆ಈΛߦ͍ͬͯΔ ̍ɽϢʔβʹมߋΛཁٻͤͣʹγεςϜมԽʹ௥ैՄೳͳ ɹɹSSHϓϩΩγαʔό ̎ɽྔࢠίϯϐϡʔλɾΞχʔϦϯάϚγϯΛ༻͍ͨݚڀߏ૝
12. 12 ssh username@<hostname or IP> SSH Client • WebαʔϏεΛࢧ͑ΔΠϯϑϥ͸ɼར༻ऀ͔Βͷଟ༷ͳཁٻ΍؀ڥͷมԽ౳ʹԠͯ͡ɼ ਝ଎͔ͭॊೈʹγεςϜߏ੒Λมߋ͢Δ͜ͱ͕ٻΊΒΕΔɽ • αʔϏε΁ͷଟ༷ͳཁٻʹԠͯ͡γεςϜߏ੒Λਝ଎ʹมߋ͍ͯ͘͜͠ͱ͕ٻΊΒΕΔ ঢ়گʹ͓͍ͯ͸ɼγεςϜͷӡ༻؅ཧ΋มߋʹ௥ैͰ͖Δඞཁ͕͋Δɽ • Ұํɼ҆શͳϦϞʔτ઀ଓαʔϏεͱͯ͠αʔό؅ཧʹ޿͘ར༻͞Ε͍ͯΔSSH͸ɼ Ϣʔβ͕ར༻͢ΔαʔόͷIPΞυϨε·ͨ͸ϗετ໊Λࢦఆͯ͠઀ଓཁٻΛૹΔ࢓૊Έ Ͱ͋Δɽ എܠɿγεςϜมԽʹ௥ैͰ͖Δӡ༻؅ཧ Ϣʔβ มߋ Server Server αʔόͷIPΞυϨεɾ ϗετ໊ͷมߋͳͲ Ϣʔβ͕มߋޙͷ৘ใ Λ஌Δඞཁ͕͋Δ
13. 13 sshr: SSHϓϩΩγαʔό γεςϜ؅ཧऀ͕ࣗ༝ʹ૊ΈࠐΈՄೳͳϑοΫؔ਺Λ༻͍ͯγεςϜมԽʹ௥ैͰ͖Δ sshr※1ͱ͍͏SSHϓϩΩγαʔόΛఏҊ SSHΫϥΠΞϯτ ssh username@hostname Ϣʔβ໊ ઀ଓઌϗετ ؅ཧσʔλ ϑοΫؔ਺ SSH ϓϩΩγαʔό αʔό܈ ※1 https://github.com/tsurubee/sshr • Ϣʔβʹ༻͍ΔΫϥΠΞϯτπʔϧͷ੍ݶ΍มߋΛ՝͞ͳ͍ • ૊ΈࠐΉϑοΫؔ਺ͷΈͷमਖ਼ͰϓϩΩγαʔόͷಈ࡞Λࣗ༝ʹม͑ΒΕΔͨΊɼ γεςϜͷ࢓༷มߋʹରͯ͠ߴ͍֦ுੑΛ༗͢Δ
14. 14 IOTS2019Ͱൃද͠·ͨ͠ 2019೥12݄5ʙ6 ೔ʹ։࠵͞Εͨୈ12ճΠϯλʔωοτͱӡ༻ٕज़ γϯϙδ΢Ϝ (IOTS2019)Ͱൃද͠·ͨ͠
15. 15 ࠓޙͷల๬ ΫϥΠΞϯτ͔ΒݟΔͱɼSSHͷϢʔβ໊ʹඥ͍ͮͨ1ͭͷαʔόʹ͔͠SSH઀ଓͰ͖ͣɼ ෳ਺ͷαʔόͷத͔Βҙਤతʹ઀ଓઌΛࢦఆͯ͠઀ଓཁٻΛૹΔ͜ͱ͕Ͱ͖ͳ͍ SSHΫϥΠΞϯτ ᶃ઀ଓཁٻ ᶄSSHϢʔβ໊ ᶅαʔόߏ੒৘ใ ؅ཧσʔλ ϑοΫؔ਺ SSH ϓϩΩγαʔό ղܾҊ ՝୊ ɾκʔϯ΍ϩʔϧͳͲͷλά৘ใΛ΋ͱʹબ୒͢Δ ɾো֐ঢ়گɾαʔόෛՙͳͲͰιʔτͯ͠ϑΟϧλϦϯά ଟछଟ༷ͳϩʔϧ΍σόΠε͕͋ΓɼͦΕΒ͕࣌ʑࠁʑͱมԽ͢ΔϦιʔεͷ؅ཧʹ͓͍ͯ ΫϥΠΞϯτ͕ͦΕΒͷঢ়گΛஞҰ೺Ѳ͢Δ͜ͱͳ͘ɼ໨తαʔόΛݕࡧͯ͠઀ଓͰ͖Δ࢓૊Έ ᶆαʔόߏ੒৘ใ ᶇ઀ଓઌαʔόΛબ୒ ᶈ઀ଓཁٻ $ ssh tsurubee@sshr +-----+----------+---------+--------+-----------+----------+ | No. | Hostname | Zone | Role | CPU usage | Status | +----------------+---------+--------------------+----------+ | 1 | server01 | tokyo | db | 90 | critical | | 2 | raspi01 | fukuoka | sensor | 70 | warning | ɿɹɹɹɹ ɹ ɿ
16. 16 ݱࡏͷݚڀςʔϚ ҎԼͷ2ͭΛ࣠ʹݚڀ׆ಈΛߦ͍ͬͯΔ ̍ɽϢʔβʹมߋΛཁٻͤͣʹγεςϜมԽʹ௥ैՄೳͳ ɹɹSSHϓϩΩγαʔό ̎ɽྔࢠίϯϐϡʔλɾΞχʔϦϯάϚγϯΛ༻͍ͨݚڀߏ૝
17. 17 ྔࢠίϯϐϡʔλͱ͸ ྔࢠίϯϐϡʔλͱ͸ɼྔࢠྗֶͷݪཧʹج͍ͮͨ৽͍͠ίϯϐϡʔλͷ֓೦Ͱ͋Δɽ ྔࢠίϯϐϡʔλͷΞΠσΞ͸ɼ1982೥ͷFeynmanͷ ࿦จ͕࢝·Γͱ͞Ε͍ͯΔɽ ैདྷίϯϐϡʔλͱͷҧ͍ɿ৘ใͷද͠ํ ϒϩοϗٿ※1 ※1 https://ja.wikipedia.org/wiki/%E3%83%96%E3%83%AD%E3%83%83%E3%83%9B%E7%90%83 ৘ใͷجຊ୯ҐɿϏοτ ྔࢠϏοτ ྔࢠྗֶ or 0 1 0 1 0·ͨ͸1ͲͪΒ͔ ͷঢ়ଶΛͱΔ 0ͱ1ͷ྆ํͷঢ়ଶ Λಉ࣌ʹͱΔ ʢॏͶ߹Θͤঢ়ଶʣ
18. 18 ྔࢠίϯϐϡʔλɾΞχʔϦϯάϚγϯ ྔࢠίϯϐϡʔλ(ήʔτํࣜ) ΞχʔϦϯάϚγϯ(ΠδϯάϚγϯ) Google IBM Microsoft IonQ σδλϧճ࿏ ௒ిಋճ࿏ ෋࢜௨ ೔ཱ D-Wave NEC ྔࢠྗֶͷݪཧΛར༻
19. 19 ྔࢠήʔτϚγϯͷݱঢ় ྔࢠϏοτ਺ͷ֦େ ྔࢠ௒ӽੑͷ࣮ূ Ϗοτ਺΋೥ʑ৳ͼ͍ͯΔ͕ɼ࣮༻తͳ໰୊ Λղ͘ʹ͸ɼࠓޙͷݚڀ։ൃ͕଴ͨΕΔɽ ग़యɿʰ1೥Ͱूੵ౓͕ڻҟతʹ޲্ͨ͠ྔࢠίϯϐϡʔλʱ https://jbpress.ismedia.jp/articles/-/54979 Google͕2019೥10݄ʹɼྔࢠίϯϐϡʔλͷܭࢉೳྗ͕ɼεʔύʔίϯϐϡʔλͳͲ ैདྷܕͷίϯϐϡʔλΛ্ճΔ͜ͱΛࣔ͢ʮྔࢠ௒ӽੑʯΛ࣮ূͨ͠ͱൃද※1 ࠷ઌ୺ͷεʔύʔίϯϐϡʔλͰ͸ղ͘ͷʹ໿1ສ೥͔͔ΔܭࢉΛGoogleͷྔࢠίϯ ϐϡʔλ͸200ඵͰղ͍ͨͱ͞Ε͍ͯΔɽ(໿16ԯഒ) ※1 F. Arute et al.: Quantum supremacy using a programmable superconducting processor, Nature, Vol. 574, 505-510 2019.
20. 20 (ྔࢠ)ΞχʔϦϯάϚγϯͷݱঢ় D-Waveͷ঎༻ԽͱԠ༻ࣄྫͷ޿͕Γ ࠃ಺اۀͷ࣮ূ࣮ݧ 2011೥ʹ঎༻ΞχʔϦϯάϚγϯD-Waveͷొ৔ ϑΥϧΫεϫʔήϯ͕๺ژͷࢢ಺ͷަ௨ौ଺ΛD-WaveΛ ׆༻ͯ͠ղফ͢Δࣾձ࣮ݧΛߦͬͨ※1 ※1 F. Neukart et al.: Traffic flow optimization using a quantum annealer, Frontiers in ICT, Vol. 4, 1-6 2017. ೔ཱ੡࡞ॴͷCMOSΞχʔϦϯάϚγϯ΍෋࢜௨ͷ σδλϧΞχʔϥ౳Λ׆༻࣮ͨ͠ূ࣮ݧ͕ߦΘΕͯ ͍Δ https://www.hitachi.co.jp/New/cnews/month/2020/01/0108.html
21. 21 ྔࢠίϯϐϡʔλɾΞχʔϦϯάϚγϯ ྔࢠίϯϐϡʔλ(ήʔτํࣜ) ΞχʔϦϯάϚγϯ(ΠδϯάϚγϯ) Google IBM Microsoft IonQ σδλϧճ࿏ ௒ిಋճ࿏ ෋࢜௨ ೔ཱ D-Wave NEC ྔࢠྗֶͷݪཧΛར༻
22. 22 ͜Ε·Ͱͷ͘͞ΒͷऔΓ૊Έ https://www.sakura.ad.jp/information/pressreleases/2018/10/18/1968198517/ 1೥Ҏ্લ͔Β೔ཱ੡࡞ॴ͕։ൃͨ͠ ΞχʔϦϯάϚγϯͷධՁΛߦͳ͖ͬͯͨ ʢ౰ݚڀॴ٠஍͕୲౰ʣ
23. 23 ૊߹ͤ࠷దԽ໰୊ ཭ࢄଟม਺ؔ਺ͷ࠷খ஋ʢ͋Δ͍͸࠷େ஋ʣ͓Αͼͦͷ࠷খ஋Λ༩͑Δม਺ͷ ૊Έ߹ͤΛٻΊΔ໰୊ ྫͱͯ͠ɼ८ճηʔϧεϚϯ໰୊ɼφοϓαοΫ໰୊ɼδϣϒγϣοϓ εέδϡʔϦϯά໰୊ͳͲ͕ڍ͛ΒΕɼଟ༷ͳ෼໺ʹ಺ࡏ͍ͯ͠Δɽ ଟ͘ͷ૊߹ͤ࠷దԽ໰୊͸ैདྷͷίϯ ϐϡʔλͰ͸ޮ཰తʹղ͘͜ͱ͕ࠔ೉Ͱ ͋Γɼ໰୊ͷղۭؒͷ୳ࡧ͕ඞཁͰ͋Δ ૊߹ͤ࠷దԽ໰୊ͱ͸ ղ͘ͷ͕೉͍͠ʁ ղͷީิ਺ʢʹܭࢉ࣌ؒʣ ࢦ਺ؔ਺త૿Ճ ʹ૊߹ͤരൃ ໰୊ͷαΠζ ϝλώϡʔϦςΟοΫ ΞϧΰϦζϜ
24. 24 ϝλώϡʔϦεςΟοΫ ϝλώϡʔϦεςΟοΫͱ͸ ಛఆͷ໰୊ͷΈΛର৅ͱ͢ΔͷͰ͸ͳ͘ɺ༷ʑͳ໰୊ʹରͯ͠ɺ ൺֱత୹࣌ؒͰۙࣅղΛޮ཰Α͘ٻΊΔղ๏ ΞϧΰϦζϜͷྫ • Ҩ఻తΞϧΰϦζϜ • ٜίϩχʔ࠷దԽ • ཻࢠ܈࠷దԽ • ྔࢠΞχʔϦϯά ਐԽܭࢉΞϧΰϦζϜ Ϋϥ΢υίϯϐϡʔςΟϯά΍ ΤοδɾϑΥάίϯϐϡʔςΟ ϯάͷจ຺Ͱͷݚڀࣄྫ΋͋Δ https://www.sciencedirect.com/science/article/pii/S1110866515000353 https://dl.acm.org/doi/10.1145/3287921.3287984
25. 25 ྔࢠΞχʔϦϯά • ྔࢠΞχʔϦϯάͱ͸ɼྔࢠྗֶͷ๏ଇΛར༻ͯ͠ɼ ͋Δछͷ৘ใॲཧΛ͢ΔͨΊͷ࿮૊ΈͰ͋Δ ྔࢠΞχʔϦϯάͱ͸ • ૊߹ͤ࠷దԽ໰୊ʹର͢ΔϝλώϡʔϦεςΟοΫ ͳղ๏ͱͯ͠஫໨͞Ε͍ͯΔ ̍ɽେن໛ͳ૊Έ߹Θͤ࠷దԽ໰୊Λߴ଎ʹղ͘͜ͱ͕ظ଴͞ΕΔ※2 ̎ɽجఈঢ়ଶ͕ॖୀ͍ͯ͠Δ৔߹ʹɼภͬͨղ͕ಘΒΕΔ※3ʢΞϯϑΣΞαϯϓϦϯάʣ ྔࢠΞχʔϦϯάͷಛ௃ ※1 T. Kadowaki and H. Nishimori: Quantum annealing in the transverse Ising model, Phys. Rev. E, Vol. 58, 5355 1998. ※2 V. S. Denchev et al.: What is the Computational Value of Finite Range Tunneling?, Phys. Rev. X, Vol. 6, 031015 2016. ※3 B. H. Zhang et al.: Advantages of Unfair Quantum Ground-State Sampling, Scientific Reports, Vol. 7, 1044 2017. ྔࢠΞχʔϦϯά͸1998೥ͷ࿦จͰ೔ຊਓ ͕ཧ࿦ΛߟҊͨ͠※1 • ΠδϯάϞσϧͷجఈঢ়ଶΛྔࢠྗֶతͳΏΒ͗Λ ར༻ͯ͠୳ࡧ͢Δ͜ͱͰ૊߹ͤ࠷దԽ໰୊Λղ͘ ΠδϯάϞσϧ
26. 26 1. ߴ଎ͳ૊Έ߹Θͤ࠷దԽͷղ๏ ※1 V. S. Denchev et al.: What is the Computational Value of Finite Range Tunneling?, Phys. Rev. X, Vol. 6, 031015 2016. ※2 ੢৿ लູ, େؔ ਅ೭: ྔࢠΞχʔϦϯάͷجૅ, ڞཱग़൛, 2018. ૊Έ߹Θͤͷ਺͕๲େͰैདྷͷख๏Ͱ͸࠷దղͷ୳ࡧʹ͕͔͔࣌ؒΔ໰୊Λ࣮༻తͳ ࣌ؒ಺Ͱղ͘͜ͱ͕ظ଴Ͱ͖Δɽ γϛϡϨʔςΟουΞχʔϦϯάʢSAʣʹൺ΂ͯ ྔࢠϞϯςΧϧϩ๏ʢQMCʣɼD-Wave͸໰୊αΠζͷ ֦େʹର͢Δ܏͖͕খ͍͞ɽ ࠷దԽ୳ࡧ·Ͱʹ͔͔Δ࣌ؒͱ໰୊ͷαΠζͷؔ܎※1 ҎԼͷΑ͏ͳٞ࿦΋͋Δ※2 ɾͦ΋ͦ΋SA͸ϝλώϡʔϦεςΟοΫͳΞϧΰϦζϜ ɹͰ͋Γɼߴ଎Ͱ͸ͳ͍ɽ ɾGPUͷ׆༻΍ฒྻॲཧͰSAɼQMC΋ߴ଎ԽͰ͖Δɽ
27. 27 2. ΞϯϑΣΞαϯϓϦϯά ϑϦʔεϐϯͷ਺͕ଟ͍εϐϯ഑ஔ͕બ୒తʹ ಘΒΕ΍͍͢※1 ※1 Y. Matsuda et al.: Ground-state statistics from annealing algorithms: quantum versus classical approaches, New Journal of Physics, Vol. 11, 073021 2009. ※2 S. Mandrà et al.: Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians, Phys. Rev. Lett., Vol. 118, 070502 2017. ※3 D. Bertsimas et al.: Robust optimization with simulated annealing, Journal of Global Optimization, Vol. 48, 323-334 2009. جఈঢ়ଶ͕ॖୀ͍ͯ͠Δ৔߹ɼऔΓ͏Δશͯͷجఈঢ়ଶ͕౳͍֬͠཰ͰಘΒΕͳ͍͜ͱ͕ ྔࢠϞϯςΧϧϩ๏※1΍D-Wave※2Ͱࣔ͞Ε͍ͯΔʢSAͰ͸౳͍֬͠཰ͰಘΒΕΔʣ QA͕ಛఆͷجఈঢ়ଶʹ౸ୡ͢Δ૬ରස౓ͷώετάϥϜ ղͷ૊Έ׵͑΍͢͞ʹܨ͛ΒΕͳ͍ͩΖ͏͔ɽ কདྷͷෆ֬ఆΛड͚ೖΕͨϩόετ࠷దԽ※3
28. 28 ITΠϯϑϥͱ࠷దԽ ෳࡶੑ͕૿͢ITΠϯϑϥ ͳͲ༷ʑͳཁҼ͕བྷΈ߹͍ɼγεςϜΛࢧ͑ΔΠϯϑϥ͸ෳࡶԽ͍ͯ͠Δɽ • ίϯςφͳͲͷԾ૝Խٕज़ͷීٴ • ෼ࢄγεςϜɾϚΠΫϩαʔϏεԽ • IoTσόΠεͷීٴʢΤοδɾϑΥάίϯϐϡʔςΟϯάʣ ITΠϯϑϥͷ࠷దԽ • ΠϯϑϥͷෳࡶԽʹ൐͍ɼϦιʔε؅ཧɼ؂ࢹɾҟৗݕ஌ɼίετ࡟ݮͳͲͷ໰୊΋ෳࡶԽ ͍ͯ͠Δɽ • ਓ͕ؒ༩͑ͨϧʔϧϕʔεͰͷ؅ཧ͸ݶք͕͋ΔͨΊɼಈతʹมԽ͢Δঢ়گΛߟྀ͠ͳ͕Β ࠷దԽ͢Δ࢓૊ΈΛ࣮ݱ͍ͨ͠ɽ Ϋϥ΢υίϯϐϡʔςΟϯά΍ΤοδɾϑΥάίϯϐϡʔςΟϯάͳͲզʑͷ෼໺Ͱ ղ͖͍ͨ૊߹ͤ࠷దԽ໰୊Λߟ͍͑ͯ͘
29. 29 1. φοϓαοΫ໰୊ ※1 C. Guerrero et al.: Genetic Algorithm for Multi-Objective Optimization of Container Allocation in Cloud Architecture, Journal of Grid Computing, Vol. 16, 113-135 2018. ※2 Y. Gao et al.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing, J. Comput. Syst., Vol. 79, 1230-1242 2013. Physical Node ෳ਺ͷ෺ཧϊʔυʹରͯ͠ίϯςφɾԾ૝ϚγϯʢVMʣΛͲͷΑ͏ʹ഑ஔ͢Δͷ͔ ໰୊ ίϯςφɾVMͷऩ༰ઃܭ͸ɺγεςϜશମͷύϑΥʔϚϯε΍৴པੑɼεέʔϥϏϦςΟʹେ͖ͳӨڹ Λ༩͑Δɽ͜Ε·ͰʹҨ఻తΞϧΰϦζϜ※1΍ٜίϩχʔ࠷దԽ※2Ͱ࠷దԽͨ͠ࣄྫͳͲ͕ଘࡏ͢Δɽ Container or VM
30. 30 2. δϣϒγϣοϓεέδϡʔϦϯά໰୊ ஍ཧతʹ෼ࢄͨ͠ίϯϐϡʔςΟϯάϦιʔεʹͲ͏λεΫΛׂΓ౰ͯΔ͔ ໰୊ ҟͳΔੑೳΛ༗͢Δෳ਺ͷϊʔυʹෳ਺ͷλεΫΛޮ཰తʹׂΓ౰ͯΔ͜ͱͰɼશλεΫͷ׬ྃ࣌ؒΛ ୹ॖ͢ΔɽλεΫಉ࢜ʹґଘؔ܎΍༏ઌॱҐ͕͋Δ৔߹΋૝ఆ͞ΕΔɽ ※1 B. M. Nguyen et al.: Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment, Appl. Sci., Vol. 9, 1730 2019. ※1 https://jp.alibabacloud.com/about/what-is-edge-computing
31. 31 3. ΫϥελϦϯά ίϯϐϡʔςΟϯάϦιʔεΛෛՙঢ়گ౳ͰΫϥελϦϯά͢Δɽ ૬ରతʹҟৗͳৼΔ෣͍Λ͍ͯ͠Δ΋ͷΛݕग़͢Δɽ ໰୊ ※1 B. Taskar et al.: Probabilistic Classification and Clustering in Relational Data, the Seventeenth International Joint Conference on Artificial Intelligence, 4-10 2001. ࣌ؒ ͭͷ఺ʹ୆ͷαʔόɼ7.ɼ*P5 σόΠε౳ͷଟ࣍ݩ࣌ܥྻσʔλͷ ৘ใ͕ೖ͍ͬͯΔΠϝʔδ https://towardsdatascience.com/semantic-similarity-classifier-and- clustering-sentences-based-on-semantic-similarity-a5a564e22304 $16ɾϝϞϦར༻཰
    ౳ͷϝτϦΫε ͦΕͧΕΛಠཱͯ͠औΓѻΘͣޓ͍ʹ૬ؔΛ࣋ͭ ؔ܎σʔλͱͯ͠ΫϥελϦϯά͢Δख๏※1΋༗ޮ Ͱ͋Δͱߟ͑Δɽ
32. 32 ࠓޙͷల๬ ॴ๬ͷ࣌ؒεέʔϧͰղ͚Δ͔ʁ ໰୊ઃఆɿΫϥ΢υίϯϐϡʔςΟϯάɼΤοδɾϑΥάίϯϐϡʔςΟϯάʹ͓͍ͯղ͖͍ͨ ɹɹɹɹɹ࠷దԽ໰୊Λఆٛ͢Δ ఆࣜԽɿ໨తؔ਺ͷఆࣜԽ΍ΠδϯάϞσϧ΁ͷམͱ͠ࠐΈ ධՁɿ֤छղ๏Λ༻͍ͯ໰୊Λղ͘ https://link.springer.com/content/pdf/10.1007/s10898-009-9496-x.pdf ग़͖ͯͨղͷ෼෍ɾੑ࣭͸Ͳ͏͔ʁ ղͷੑ࣭͕ύϥϝʔλมಈʹରͯ͠ؤڧͰ͋Γɼܥͷ҆ఆੑΛҡ࣋͠ ͨ··༰қʹ૊Έ׵͑Մೳͳϩόετੑ΋ͭ࠷దԽ͕࣮ݱͰ͖Ε͹ɼ ಈతʹมԽ͢ΔγεςϜ؀ڥʹ͓͍ͯɼকདྷͷෆ֬ఆੑΛड͚ೖΕͭ ͭܧଓతʹ࠷దԽՄೳͳ࢓૊Έ͕࣮ݱͰ͖ΔͷͰ͸ͳ͍͔
33. 33 ·ͱΊ • ͘͞ΒΠϯλʔωοτݚڀॴͰ6೥ͿΓʹݚڀʹ௅ઓ͠·ͨ͠ɽ • ࣗ཯෼ࢄڠௐతͳݚڀॴͰշదͳݚڀϥΠϑΛૹ͍ͬͯ·͢ɽ • ࠓޙ͸ɼSSHؔ࿈͓ΑͼྔࢠίϯϐϡʔλɾΞχʔϦϯάؔ࿈ͷݚڀΛ ਐΊ͍͖ͯ·͢ɽ • ·ͨࠓޙͷݚڀ੒ՌΛͲ͔͜Ͱ͓࿩͠͠·͢ʂ
34. 34 ँࣙ ຊൃදͰ঺հͨ͠sshrͷ։ൃΛਐΊΔʹ͋ͨΓɺଟେͳΔ͝ࢧԉͱ ͝ॿݴΛࣀΓ·ͨ͠GMOϖύϘגࣜձࣾͷϗεςΟϯάࣄۀ෦ͷ օ༷Λ͸͡Ίଟ͘ͷํʑʹް͘ײँΛਃ্͛͠·͢ɽ