分散アプリケーションの高信頼化のための 運用技術に関する研究 / A Study on Operation Technology for High Reliability of Distributed Applications

京都大学大学院 情報学研究科 知能情報学専攻 
中間発表（15min）。

Transcript

1. ෼ࢄΞϓϦέʔγϣϯͷߴ৴པԽͷͨΊͷ 
 ӡ༻ٕज़ʹؔ͢Δݚڀ ɾΫϥ΢υΛ࢖༻ͨ͠ΞϓϦέʔγϣϯͷ৴པੑΛ޲্ͤ͞Δ ΞʔΩςΫνϟΛݚڀ͍ͯ͠Δɻ ɾಛʹɺγεςϜͷӡ༻ঢ়گΛࣔ͢σʔλΛޮ཰తʹऩू͢Δख ๏ʹߩݙͨ͠ɻ ɾݱࡏ͸ɺऩू͞Εͨσʔλ͔ΒɺγεςϜʹൃੜ͢Δҟৗͷݪ ҼΛਝ଎ʹಛఆ͢ΔͨΊͷσʔλ෼ੳख๏Λݚڀ͍ͯ͠Δɻ ௶಺ ༎थ 
 ژ౎େֶେֶӃ ৘ใֶݚڀՊ ஌ೳ৘ใֶઐ߈ ത࢜ޙظ՝ఔ 2೥ 
 தؒൃද 2021೥8݄5೔
2. 2 1. Ϋϥ΢υ্ͷ෼ࢄΞϓϦέʔγϣϯ Ϋϥ΢υ্ͷ෼ࢄΞϓϦέʔγϣϯͷෳࡶԽ ৴པੑͷ՝୊: ӡ༻σʔλྔͷ૿Ճ 
 2. ݚڀख๏ ӡ༻σʔλͷऩूͷߴޮ཰Խ • [ݚڀ੒Ռ1] ଟ਺ͷ࣌ܥྻσʔλΛૠೖɾอଘ͢ΔͨΊͷσʔλϕʔε • [ݚڀ੒Ռ2] ωοτϫʔΫ௨৴ͷґଘؔ܎ͷ௿Φʔόϔουऩू ӡ༻σʔλͷղੳ • [ݚڀ੒Ռ3] ࣌ܥྻσʔλͷத͔ΒݪҼ਍அʹ༗༻ͳܥྻΛநग़ 3. ·ͱΊ/ݚڀ࣮੷ ൃද಺༰ͷߏ੒
3. 3 Ϋϥ΢υ্ͷ෼ࢄΞϓϦέʔγϣϯͷෳࡶԽ ɾ ɾ ɾ ΞΫηε૿Ճ εέʔϧΞ΢τʹΑΔ ϗετ਺ͷ૿Ճ ػೳͷ૿Ճ ΑΓେن໛ͳ 
 ෼ࢄΞʔΩςΫνϟ ϛυϧ΢ΣΞͷ૿Ճ RDBαʔό Ωϟογϡ 
 αʔό ݕࡧαʔό Webαʔό ωοτϫʔΫ 
 αʔϏε TCP/UDP ɾ ɾ ɾ ϝσΟΞɺECαΠτɺSNSɺIoTͳͲΛߏ੒͢Δ෼ࢄΞϓϦέʔγϣϯ
4. 4 ɾίϯϙʔωϯτ਺ͷ૿େʹΑΓɺӡ༻σʔλྔ͕૿Ճ ɾσʔλऩूͷͨΊͷܭࢉػͷෛՙ͕૿Ճ ɾγεςϜ؅ཧऀͷೝ஌ෛՙ͕૿Ճ ෳࡶԽͨ݁͠Ռͷ৴པੑͷ௿Լ ӡ༻σʔλͷ 
 ऩू ɾෛՙঢ়گΛࣔ࣌͢ܥྻσʔλ ɾωοτϫʔΫ௨৴ͷґଘؔ܎ ɾςΩετϩά ৴པੑͷ௿Լ΁ ো֐෮چ࣌ؒͷ૿େ
5. 5 ɾӡ༻σʔλͷऩूΛߴޮ཰Խ͢Δ ܭࢉػͷෛՙΛ௿ݮ [ݚڀ੒Ռ1] ࣌ܥྻσʔλΛߴޮ཰ʹૠೖɾอଘ͢Δσʔλϕʔε [ݚڀ੒Ռ2] ωοτϫʔΫ௨৴ͷґଘؔ܎ͷ௿Φʔόϔουऩू ໨త: ӡ༻σʔλͷऩूͱղੳͷߴ౓ԽʹΑΓ৴པੑΛ޲্ ͍ͣΕ΋࣮؀ڥ΁ͷద༻༰қੑΛߟྀ͢Δ ɾӡ༻σʔλΛղੳ͠ɺো֐ରԠʹ༗༻ͳಎ࡯ΛಘΔ ೝ஌ෛՙΛ௿ݮ [ݚڀ੒Ռ3] ࣌ܥྻσʔλͷத͔ΒݪҼ਍அʹ༗༻ͳܥྻΛநग़ ʢ↪࿦จࢽ౤ߘதʣ ʢ↪࿦จࢽ࠾࿥ࡁʣ ʢ↪ࠃࡍձٞ౤ߘ४උதʣ
6. 6 ɾҰൠʹɺσΟεΫϕʔεDB͸ɺࡧҾߏ଄ʹฏߧ໦Λ࢖༻͢Δ ɾ՝୊ɿܥྻ਺(n)ͷ૿Ճʹରͯ͠ɺܭࢉྔ͕O(log n)Ͱ૿Ճ͢Δ ɾ؅ཧͷෛ୲Λܰݮ͢ΔͨΊʹɺطଘͷDB্ʹߏ੒͍ͨ͠ [ݚڀ੒Ռ1] ࣌ܥྻσʔλͷऩूͷߴޮ཰Խ ௶಺༎थ΄͔, HeteroTSDB: ҟछ෼ࢄKVSؒͷࣗಈ֊૚ԽʹΑΔߴੑೳͳ࣌ܥྻσʔλϕʔε, ৘ใॲཧֶձ࿦จࢽ, Vol.62, No.3, pp.818-828, 2021೥3݄. ϝϞϦ 
 ϕʔεDB σΟεΫ 
 ϕʔεDB ϋογϡද O(k) ฏߧ໦ O(log n) σʔλ఺ૠೖ dݸͷσʔλ఺Λ஝ੵޙʹ 
 ϑϥογϡॻ͖ࠐΈ ղܾɿϝϞϦϕʔεDBͱσΟεΫϕʔεDBͷ֊૚ԽΛఏҊ M (insertions/s) M / d (insertions/s)
7. 7 [ݚڀ੒Ռ1] 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) ௶಺༎थ΄͔, HeteroTSDB: ҟछ෼ࢄKVSؒͷࣗಈ֊૚ԽʹΑΔߴੑೳͳ࣌ܥྻσʔλϕʔε, ৘ใॲཧֶձ࿦จࢽ, Vol.62, No.3, pp.818-828, 2021೥3݄.
8. (౤ߘத) Y. Tsubouchi, et al., Low Overhead TCP/UDP Socket-based Tracing for Discovering Network Services Dependencies, Journal of Information Processing 2022. [ݚڀ੒Ռ2] ωοτϫʔΫ௨৴ͷґଘؔ܎औಘͷޮ཰Խ . . . 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໨ 8
9. 9 ɾఏҊख๏͸ɺCPUར༻཰͸2.2%ҎԼɻ ɾϑϩʔ਺ͷ૿େʹରͯ͠ɺϑϩʔूଋʹΑΓɺ 
 CPUར༻཰Λ௿͘ҡ͍࣋ͯ͠Δ [ݚڀ੒Ռ2] ϑϩʔ਺ͷ૿େʹର͢ΔCPU࢖༻ྔͷมԽ ఏҊख๏ (౤ߘத) 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)
10. εςοϓ1: ظؒதͷมಈͷ౓߹͍͕খ͍͞ܥྻΛআڈ εςοϓ2: άϥϑͷܗঢ়͕ྨࣅ͢Δ΋ͷ͔Β୅දܥྻΛநग़ 10 ɾ՝୊: ίϯϙʔωϯτ͝ͱʹɺಛ௃ྔͱͯ͠ܥྻͷछผʢCPU࢖༻ɺϝϞϦ࢖ ༻ɺωοτϫʔΫଳҬɺ…ʣΛࢦఆ͢Δඞཁ͋Γ ɾղܾ: ؔ࿈͢ΔશܥྻΛೖྗͱͯ͠༩͑ɺݪҼ਍அʹ༗༻ͳܥྻΛߴ଎ʹநग़ [ݚڀ੒Ռ3] ࣌ܥྻσʔλ͔ΒݪҼ਍அʹ༗༻ͳܥྻΛநग़ ҟৗ 
 ݕ஌ YES ࣌ܥྻσʔλͷऩू ݪҼ਍அ 
 ҼՌάϥϑΛ 
 ߏங [AutoMAP, Microscope,…] ఏҊ ࣌ܥྻσʔλͷ 
 ࣍ݩ࡟ݮ ௶಺༎थ΄͔, TSifter: ϚΠΫϩαʔϏεʹ͓͚Δੑೳҟৗͷਝ଎ͳ਍அʹ޲͍ͨ࣌ܥྻσʔλͷ࣍ݩ࡟ݮख๏, Πϯλʔωοτͱӡ༻ٕज़γϯϙδ΢Ϝ࿦จू, 2020೥.
11. 11 [ݚڀ੒Ռ3] ܥྻ࡟ݮੑೳͱߴ଎ੑ ɾCPUίΞ਺4ɺܥྻ਺100k εςοϓ1 
 ܥྻ୯Ґͷআڈ εςοϓ2 ྨࣅܥྻͷू໿ ߹ܭ 
 ࣮ߦ࣌ؒ 54.41 (sec) 8.68 (sec) 63.09 (sec) ߴ଎ੑ ܥྻ࡟ݮੑೳ ҟৗ஫ೖ ίϯϙʔωϯτ 
 A ίϯϙʔωϯτ 
 B CPUաෛՙ 1545/201/122 
 92.1% 1541/156/89 
 94.2% ωοτϫʔΫ 
 ஗Ԇ 1596/248/128 91.9% 1543/262/128 91.7% ɾܥྻ࡟ݮੑೳ͕ɺߴ͍ਫ਼౓ͷݪҼ ਍அͷͨΊʹे෼͔Ͳ͏͔͸ɺࠓ ޙݕূ༧ఆ ɾݱ৔ͷো֐ରԠʹద༻͢Δ্Ͱɺ े෼ͳੑೳΛಘΒΕͨ ௶಺༎थ΄͔, TSifter: ϚΠΫϩαʔϏεʹ͓͚Δੑೳҟৗͷਝ଎ͳ਍அʹ޲͍ͨ࣌ܥྻσʔλͷ࣍ݩ࡟ݮख๏, Πϯλʔωοτͱӡ༻ٕज़γϯϙδ΢Ϝ࿦จू, 2020೥.
12. 12 ɾΫϥ΢υ্ͷ෼ࢄΞϓϦέʔγϣϯͷ৴པੑΛ޲্ͤ͞ΔͨΊͷΞʔΩς ΫνϟΛݚڀ͍ͯ͠Δɻ ɾಛʹɺγεςϜͷӡ༻σʔλΛߴޮ཰ʹऩू͢Δख๏ʹߩݙͨ͠ɻ ɾ࣌ܥྻσʔλ ɾωοτϫʔΫ௨৴ͷґଘσʔλ ɾݱࡏ͸ɺऩू͞Εͨσʔλ͔ΒɺγεςϜʹൃੜ͢ΔҟৗͷݪҼΛಛఆ͢ Δख๏Λݚڀ͓ͯ͠Γɺ੒Ռ͕Ͱͭͭ͋Δɻ ɾ͜ΕΒͷߩݙ͸ɺ৴པੑͷ޲্ʹد༩͢Δ΋ͷͰ͋Δɻ ·ͱΊ
13. 13 ࣮຿্Ͱ͸ɺӡ༻σʔλͷղੳͷࡍʹɺݱ࣮తʹऩूՄೳͳӡ༻σʔλͷ छྨ΍ཻ౓Λ૝ఆ͢Δɻ ֶज़తߩݙ σʔλͷऩूͱղੳͷ2ஈ֊Λ၆ᛌ্ͨ͠Ͱɺ࣮؀ڥ΁ͷద༻ੑͷߴ͍ ΞʔΩςΫνϟΛఏࣔ͢Δɻ ࣮຿্ͷ՝୊ ֤։ൃɾӡ༻૊৫ͷݸผͷࣄ৘ʹґଘ͢Δ σʔλͷऩूͱղੳ͸ݸผʹٞ࿦͞Ε͍ͯΔ طଘ࿦จͷ՝୊
14. 14 ɾ Y. Tsubouchi, M. Furukawa, R. Matsumoto, Low Overhead TCP/UDP Socket-based Tracing for Discovering Network Services Dependencies, Journal of Information Processing (JIP), 2022. ͜Ε·Ͱͷݚڀ੒Ռ: 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. ʢ౤ߘதʣ 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-1ܭ ࢉγεςϜͷSSE໋ྩʹΑΔߴεϧʔϓοτԽ, ిࢠ৘ใ௨৴ֶձ࿦จࢽ D, 96(10), pp.2101-2109 2013೥10݄. ʢຊൃදʹؚ·ͣʣ