ࠃࡍձٞ IEEE SERVICES/CLOUD 2020 ࢀՃใࠂ ௶಺ ༎थ@͘͞ΒΠϯλʔωοτ 2020/10/28

2 1. IEEE SERVICES/CLOUD 2020ͷ֓ཁ 2. Best Paper Awardड৆࿦จͷ঺հ 3. ௌߨͨ͠ݚڀൃදͷ঺հ 4. ΦϯϥΠϯձ৔ͷ༷ࢠͱޮՌతͳௌߨͷͨΊͷ޻෉ 5. ૯ׅͱདྷ೥ͷ։࠵༧ࠂ ໨࣍

1. IEEE SERVICES/CLOUD 2020ͷ֓ཁ

4 IEEE SERVICES 2020 ɾ2020೥10݄18-24 ๺ژ → ΦϯϥΠϯ։࠵ ɾݩདྷ͸7݄։࠵͕ͩͬͨCOVID-19ͷӨڹʹΑΓԆظ͞ΕͨͷͪʹΦϯ ϥΠϯ։࠵ͱͳͬͨ ɾIEEE Computer Society Technical Committee on Services Computing (TC-SVC) ओ࠵ ɾαʔϏείϯϐϡʔςΟϯάʹؔ࿈͢Δෳ਺ͷࠃࡍձٞͷڞ࠵Πϕϯτ ɾ2004೥͔Β։࠵͞Ε͍ͯΔ

5 IEEE SERVICES 2020಺ͷ֤ࠃࡍձٞ ෼໺ ࠾୒཰ Core Rank IEEE CLOUD Ϋϥ΢υίϯϐϡʔςΟϯά IaaSɺPaaSͳͲ 53/256 = 20.7% (regular) 13 papers (short) B IEEE ICWS WebϕʔεͷαʔϏεͷϥΠϑαΠ Ϋϧݚڀͱ؅ཧ 58/321 = 19% (regular) 20 papers (short) A IEEE SCC αʔϏείϯϐϡʔςΟϯά 41/200 = 20.5% (regular) 11 papers (workshop) 14 papers (WIP) A IEEE SMDS σʔλۦಈܕΞϓϦέʔγϣϯ σʔλ෼ੳɺσʔλ඼࣭ No data - IEEE EDGE ΤοδίϯϐϡʔςΟϯά 23/61 = 37.7% (regular) - CLOUD Λத৺ʹௌߨ

6 Keynotes (1) On Odor Reproduction, and How to Test For It by David Harel, a Vice President of the Israel Academy of Sciences and Humanities (2) Cloud Native Database Systems for Enterprise Applications by Feifei Li, a Vice President of Alibaba Group (3) On the Future of Smart Transportation with AI by Ozan Tonguz, a professor at Carnegie Mellon University (4) Digital Health and Beyond: What Computing Can Do to Transform Health by Wendy Nilsen, a Program Director in the Computer and Information Science and Engineering Directorate at NSF (5) IT Services after COVID-19 by Dr. Dong Xie, a CTO of IBM Greater China Group and Director of IBM China Research Lab (6) Participatory Systems in the Digital Era: A Services Computing Perspective by Valérie Issarny, Director of Research at INRIA.

7 ΦϯϥΠϯ։࠵ ɾࢀՃొ࿥අ༻͸ɺIEEEձһͰ͋Ε͹ૣظొ࿥Ͱ $75ɻ ɾUnderlineͱ͍͏ΦϯϥΠϯձٞγεςϜΛར༻ɻ ɾ๺ژ࣌ؒͰ։࠵ɻͨͩ͠ɺனա͔͗Β༦ํ·Ͱٳܜ͕ೖΓɺਂ໷ଳʹ ΋ηογϣϯ༧ఆ͕૊·Ε͍ͯͨɻ ɾࣄલ഑෍FAQʹΑΔͱηογϣϯͷ࿥ըΛޙ೔ެ։༧ఆͷ͜ͱɻ

8 IEEE CLOUD 2020ͷ෼໺ Cloud as a Service • IaaS, PaaS, and SaaS • Function as a Service • Network as a Service • Storage as a Service • Everything as a Service Cloud Infrastructure • Cloud Computing System & Architectures • Edge Computing System & Architectures • Cloud-centric Network Architectures • Storage & Data Architectures • Hybrid-clouds & Multi-clouds Integration Cloud Applications • Large Scale Cloud Applications • Terminal-Edge-Cloud Applications • 5G/6G Enhanced Edge/Cloud Applications • Social & Mobile Cloud Applications • Innovative Cloud Applications Cloud Management and Operations • Distributed & Parallel Query Processing • Resource, Energy & Data Management • Cloud Metering & Monitoring • Containers & Serverless Computing • SDN, NFV, & Data Center Network • Cloud Service Adaptation & Automation • Cloud Federation & Service Composition Cloud Trustworthiness • Access Control, Authorization, & Authentication • Assurance, Audit, Certification, Compliance • Fault Tolerance, High Availability, & Reliability • Cryptographic Algorithms and Protocols • Cloud Security and Privacy • Trusted Cloud Environments https://conferences.computer.org/cloud/2020/cfp/ ΑΓҾ༻ ӡ༻؅ཧ ηΩϡϦςΟ ΞϓϦέʔγϣϯ ΠϯϑϥͷΞʔΩςΫνϟ αʔϏεͱͯ͠ͷΫϥ΢υ

9 IEEE CLOUD 2020ͷηογϣϯฤ੒ ɾService Prediction & Optimization ɾEdge Applications x 2 ɾEdge Caching & Computation Offloading ɾEdge-Cloud Collaboration ɾCloud & Edge Management ɾCloud Applications x 4 ɾCloud Management x 2 ɾCloud Management & Governance ɾSDN and NFV ɾNetwork and Storage Optimization ɾDeep Learning & Federated Learning ɾCloud Modeling x 2 ɾCloud Performance & Maintenance ɾCloud Security ɾCloud Task Scheduling ɾCloud Performance Evaluation ɾServerless Computers and Containers ɾMicroservices & Containers ɾOrchestration & Data Analysis ΤοδίϯϐϡʔςΟϯάɺਂ૚ֶशɺαʔ όϨε&ίϯςφͳͲ͕ϗοτͳτϐοΫ

10 ɾ๺ژ։࠵ͳͷͰɺதࠃͷେֶʹॴଐ͢ΔํʑʹΑΔൃද͕໨ཱͬͨ ɾͦͷଞɺUSAɺϤʔϩούݍɺΠϯυɺΦʔετϥϦΞͳͲ ɾ೔ຊࠃ಺ͷ૊৫ʹॴଐ͢ΔஶऀʹΑΔൃද͸3݅ ɾIBM Research - Tokyo x 1ɺ۝भ޻ۀେֶ x 2 ɾ֤ࠃͷIBM ResearchॴଐʹΑΔൃද͕໨ཱͬͨ ɾΫϥ΢υࣄۀऀॴଐͷൃද͸3݅ͱগͳΊ (Alibaba Cloud,IBM Cloud) ൃද͞ΕΔݚڀͷஶऀͷॴଐ

11 ɾର৅γεςϜ: Ϋϥ΢υ্ͷϚΠΫϩαʔϏεɺ൓Ԡతʹεέʔϧ͢ ΔαʔόϨείϯϐϡʔςΟϯάɺଟ਺ͷΤοδͱΫϥ΢υͷ࿈ܞͱ ͍ͬͨಈత͔ͭ෼ࢄͨ͠ෳࡶͳγεςϜߏ੒ ɾ໰୊ҙࣝ: γεςϜ؅ཧऀʹΑΔϧʔϧϕʔεͷઃܭͷݶք ɾΞϓϩʔν: ଴ͪߦྻཧ࿦΍਺ཧ࠷దԽ໰୊ͳͲͷݹయతͳ਺ཧϞσ ϧͷద༻ʹՃ͑ͯɺػցֶशͱਂ૚ֶशΛద༻͢Δ ɾ৽ͨʹML/DLΛద༻͢Δͱ͍͏ΑΓɺ͢ͰʹͦΕΒΛద༻ͨ͠ઌߦ ݚڀͷ՝୊Λղ͘ͱ͍͏ஈ֊ʹೖ͍ͬͯΔ ɾ൒਺Ҏ্͕͜ͷखͷൃදͩͬͨΑ͏ʹײͨ͡ʢஶऀ؍ଌʣ Ұ෦ͷݚڀൃදʹڞ௨͢Δ໰୊ઃఆ΍ݚڀख๏

2. Best Paper Awardड৆࿦จͷ঺հ

13 IEEE CLOUD 2020 Best Paper Award Skedulix: Hybrid Cloud Scheduling for Cost-Efficient Execution of Serverless Applications Anirban Das (Rensselaer Polytechnic Institute), Andrew Leaf (Rensselaer Polytechnic Institute), Carlos A. Varela (Rensselaer Polytechnic Institute), Stacy Patterson (Rensselaer Polytechnic Institute)

14 ɾैདྷͷΞϓϦέʔγϣϯ͸ɺωοτϫʔΫI/OΛ଴ͪड͚Δৗறϓϩ ηεͰ͋ΔʮωοτϫʔΫαʔόʯʢWebαʔόͳͲʣͷܗଶΛͱΔ ɾαʔόͰ͸ͳ͘ɺೖྗΛड৴ͨ͠ͱ͖͚ͩɺॲཧ಺༰Λ಺แ͢Δ Functionʢؔ਺ʣ͕ίϯςφϓϩηεͱͯ͠ىಈͯ͠ɺδϣϒΛ࣮ߦ ͢ΔΞϓϦέʔγϣϯϞσϧɻ ɾΫϥ΢υࣄۀऀ͕Functionͱ͍͏ܗଶ·ͰΞϓϦέʔγϣϯ࣮ߦج൫ Λந৅Խ͍ͯ͠Δͱ΋ݴ͑Δɻ αʔόʔϨείϯϐϡʔςΟϯάʢFunction asSʣ

15 ɾϓϥΠϕʔτΫϥ΢υͰ΋OSSʹΑΓFaaSج൫ΛߏஙՄೳ ɾ͔͠͠ɺϓϥΠϕʔτΫϥ΢υ͸Ϧιʔε͕ݶΒΕ͍ͯΔͨΊɺϋΠ ϒϦουΫϥ΢υΛ࠾༻͢Δ͜ͱ͕͋Δɻ ϓϥΠϕʔτΫϥ΢υͰ΋εέʔϧ͍ͤͨ͞ Private Cloud Public Cloud Incoming jobs Additional jobs Function ɾϓϥΠϕʔτΫϥ΢υͷ ϓϥΠόγʔɺੑೳɺίε τ໘Ͱͷར఺ΛڗडՄೳ ɾϐʔΫ࣌ͷෛՙ૿େʹඋ ͑ͨϋʔυ΢ΣΞͷҡ͕࣋ ෆཁ ௿ίετ ߴεέʔϥϏϦςΟ

16 ఏҊख๏ Skedulix ͷ֓ཁ ɾ໨త͸ϋΠϒϦουϓϥοτϑΥʔϜ্Ͱػೳ࣮ߦΛεέδϡʔϦϯ ά͠ɺύϒϦοΫΫϥ΢υͷར༻ίετΛ࠷খݶʹ཈͑ͭͭɺϢʔβ ͕ࢦఆ࣮ͨ͠ߦ࣌ؒͷظݶΛຬͨ͢͜ͱ ɾ͜ͷεέδϡʔϦϯάͱׂΓ౰ͯ໰୊ͷܗࣜԽ͸ɺ ࠞ߹੔਺ઢܗ໰ ୊ͱ࣮ͯ͠ݱ͞ΕɺNPࠔ೉Ͱ͋Δ͜ͱ͕ূ໌͞Ε͍ͯΔʢ෇࿥ʣ ɾSkedulix͸ɺؔ਺࣮ߦ࣌ؒͱωοτϫʔΫ஗Ԇͷ༧ଌϞσϧΛ༻͍ ͯɺίετ࠷খԽͷͨΊʹ֤ػೳͷॱংͱ഑ஔΛಈతʹܾఆ͢Δᩦཉ ͳΞϓϩʔνΛ࠾༻͢Δ ɾϓϥΠϕʔτΫϥ΢υͷΈͱൺֱ͠ɺ40.5%ͷίετ௿ݮͱ࠷େ 1.92ഒͷߴ଎ԽΛୡ੒ͨ͠

17 ΞϓϦέʔγϣϯΛDAGͱͯ͠ϞσϧԽ ɾෳ਺ͷFunctionͷ࿈ͳΓʹΑΓΞϓϦέʔγϣϯ͕ߏ੒͞ΕΔ ɾDAGͷ֤ϊʔυ͸Function࣮ߦΛࣔ͢εςʔδ ɾϓϥΠϕʔτΫϥ΢υͰ͸֤εςʔδͷϨϓϦΧ਺͸ݻఆ ɾύϒϦοΫΫϥ΢υ͸ແ੍ݶͷ༰ྔͰ͋Γɺੑೳ௿Լ͸͠ͳ͍ͱ͍͏ ԾఆΛஔ͘

εέδϡʔϥ 0. ճؼϞσϧʹΑΓ δϣϒͷ࣮ߦ࣌ؒΛ༧ ଌ͢ΔϞσϧΛߏங 1. όονδϣϒͷ ΩϡʔΠϯά 2. εέδϡʔϦϯάΞ ϧΰϦζϜͷ࣮ߦ δϣϒͷ֤εςʔδ͝ ͱʹධՁ 18 εέδϡʔϥͷ֓ཁ

19 ॴײ ɾαʔόʔϨεͷϋΠϒϦουΫϥ΢υʹ͓͚ΔεέδϡʔϦϯά໰୊ ͱ͍͏৽͍͠໰୊ઃఆͰ͋Δ͜ͱ ɾ໰୊Λࠞ߹ઢܗܭը໰୊ʹམͱ͠ࠐΊͯɺͳ͓͔ͭNPࠔ೉Ͱ͋Δ͜ ͱΛཧ࿦తʹ͍ࣔͯ͠Δ͜ͱ ɾαʔόϨεͱ͍͏ந৅ԽʹΑΓ໰୊ΛఆࣜԽ͠΍͘͢ͳ͍ͬͯΔ͜ͱ ɾFunctionδϣϒΛ࣮ߦͯ͠༧Ί܇࿅͢Δͱ͍ͬͨख͕ؒ͋Δ ɾAWS͔ΒͷΞ΢τό΢ϯυτϥϑΟοΫίετ͕ߟྀ͞Ε͍ͯͳ͍ʁ ྑ͍෦෼ ݒ೦఺

3. ௌߨͨ͠ݚڀൃදͷ঺հ

21 Deep Unsupervised Workload Sequence Anomaly Detection with Fusion of Spatial and Temporal Features in the Cloud Mengqing Wang, Zhihua Zhang, and others, IEEE CLOUD 2020. ࣗ෼ͷݚڀʹؔ࿈͢Δൃද Root-Cause Metric Location for Microservice Systems via Log Anomaly Detection Lingzhi Wang, Nengwen Zhao, and others, IEEE ICWS 2020. RAD: Detecting Performance Anomalies in Cloud-Based Web Services Joydeep Mukherjee, Alexandru Baluta, and others, IEEE CLOUD 2020. ҟৗݕ஌ ҟৗݕ஌ ҟৗݕ஌ͱ ݪҼಛఆ

22 ɾखಈͰᮢ஋Λઃఆ͸ෛՙγʔέϯεͷछྨ͕ଟ ͍ͱ೉͍ͨ͠ΊɺҟৗΛڭࢣͳ͠Ͱࣗಈݕग़ ɾෳ਺ͷෛՙγʔέϯεͷมԽύλʔϯʹదͨ͠ڭ ࢣͳ͠ͷҟৗγʔέϯεݕग़ϞσϧΛͲͷΑ͏ʹ ߏங͢Δ͔͕՝୊ ɾσΟʔϓχϡʔϥϧωοτϫʔΫΛ༻͍ͯɺγʔ έϯεͷຊ࣭తͳಛ௃Λัଊ͠ɺҟৗγʔέϯε ͷແؔ܎ͳಛ௃Λ෼཭͢Δ ɾۭؒతಛ௃->CNNɺ࣌ؒతಛ௃->BiLSTM Deep Unsupervised Workload Sequence Anomaly Detection with Fusion of Spatial and Temporal Features in the Cloud

23 ɾෳࡶԽͨ͠γεςϜͷҟৗͱͦ ͷݪҼΛࣗಈతʹݕग़͍ͨ͠ ɾ֎෦γεςϜͱͷڝ߹ͳͲɺγ εςϜԼҐ૚ͷϝτϦοΫ͚ͩͰ ͸ҟৗͷݕग़͕ࠔ೉ ɾ্Ґ૚ͷܭଌ͸खؒ΍Φʔό ϔου͕͔͔Δ RAD: Detecting Performance Anomalies in Cloud-Based Web Services ɾVM͔ΒURLͱγεςϜϝτϦοΫΛܧଓతʹ؂ࢹ͠ɺΩϡʔΠϯά ωοτϫʔΫϞσϧΛߏங͢Δ ɾϞσϧͰ༧ଌͨ͠Ԡ౴࣌ؒΛɺҟৗ͕ͳ͍৔߹ͷϞσϧͰ༧ଌ͞Εͨ ϕʔεϥΠϯͱͳΔԠ౴࣌ؒͱൺֱͯ͠ҟৗݕ஌Λߦ͏

24 ɾϚΠΫϩαʔϏεͰ ͸ɺαʔϏεؒͷো֐ ఻ൖʹΑΓɺͲͷαʔ Ϗε͕ݪҼ͔ಛఆ͢Δ ͜ͱ͸ࠔ೉ ɾؔ࿈ݚڀ͸͢΂ͯϝτ ϦοΫؒͷ૬ؔؔ܎΍ ϝτϦοΫͱނোͷ૬ ؔؔ܎ΛΈ͍ͯΔ͕ɺ ϩά΋ॏཁ Root-Cause Metric Location for Microservice Systems via Log Anomaly Detection ɾDeeplog[8]ʹΑΓࢉग़ͨ͠ϩάͷҟৗείΞͱ ϝτϦοΫΛ੔߹ͤͯ͞ɺ૬ޓ৘ใྔʹج͍ͮͨ ૬ؔ෼ੳΛߦ͏ ɾ਺ඦͷࢦඪͷத͔Βฏۉ্ͯ͠Ґ15Ґ·Ͱʹࠜ ຊݪҼΛಛఆͰ͖ͨ

4. ΦϯϥΠϯձ৔ͷ༷ࢠͱ ޮՌతͳௌߨͷͨΊͷ޻෉

26 ΦϯϥΠϯձ৔ͷ༷ࢠ ɾௌऺͷਓ਺͸ɺKeynoteͰ΋20ਓલޙɻฏۉ10ਓऑɺਓؾ͕ͳ͚Ε ͹1,2ਓͳͲɻඇৗʹগͳ͍ਓ਺Ͱ͋Δͱײͨ͡ɻʢஶऀ؍ଌʣ ɾ࠲௕͕ෆࡏͷηογϣϯ͕͋ͬͨɻ࠲௕ͱൃදऀͷ3ਓ͏ͪ2ਓ͕͍ ͳ͍͜ͱ΋͋ͬͨɻ ɾUnderlineͷ഑৴ػೳͷ࢖͍উखʹ೉͋ΓɻԻ੠ͱΧϝϥͷڞ༗ͷ On/Offঢ়ଶΛ೺ѲͰ͖ͣɺมߋ͢Δʹ͸Ұ୴ୀग़͢Δඞཁ͕͋ͬͨɻ ɾ࠲௕Ҏ֎ͷௌऺʹΑΔ࣭໰͸ɺUnderlineͷQ&Aཝʹॻ͖ࠐ·ΕΔ͜ ͱ͕ଟ͔ͬͨɻ ɾ࿥ը഑৴࣌ʹ͏·͘Ի੠ͱը໘Λ഑৴Ͱ͖ͳ͍ɺԻׂ͕Ε͍ͯΔɺԻ ͕খ͍͞ɺԻ·ͨ͸εϥΠυ഑৴్͕தͰ్੾ΕΔͳͲͷτϥϒϧ

27 ௌߨͷͨΊͷ޻෉: ༧ߘͷϦʔσΟϯά ɾௌߨ༧ఆηογϣϯͷཁ໿ͱΠϯτϩμΫγϣϯͷষͷจষΛɺ຋ ༁αʔϏεDeepLʹ͔͚Δ ɾ຋༁݁ՌΛϊʔτΞϓϦʢNotionʣʹه࿥͢Δ ɾಉ࣌ʹௌߨ͍ͯͨ͠ಉ྅ʹڞ༗͢Δ Πϯϓοτྔ͕ଟ͍ͷͰɺ೴ͷෛ ୲͕ߴ͍ͨΊɺ೔ຊޠͰ༧शͨ͠ ݁Ռతʹ͔ͬ͠Γ༧शΛͯ͠ൃද Λௌ͚ͨ

28 ௌߨͷͨΊͷ޻෉: ൃදͷϦεχϯά ɾԻ੠ॻ͖ى͜͠αʔϏεOtterʹΑΓϦΞϧλΠϜʹॻ͖ى͜͠ ɾखݩ୺຤ͷԻ੠ग़ྗΛೖྗͱͯ͠ϧʔϓόοΫͤ͞Δඞཁ͕͋Δ ɾmacOSͷ৔߹ͷઃఆํ๏͸ https://www.notion.so/yuuk1/Listening-Talks-a95698a888634120a3e285c3085ab4f2

5. ૯ׅͱདྷ೥ͷ։࠵༧ࠂ

30 ૯ׅ ɾIEEE CLOUD 2020Λத৺ʹɺ֓ཁɺड৆࿦จɺௌߨͨ͠ݚڀൃදɺ ͓ΑͼɺΦϯϥΠϯௌߨͷ՝୊ͱ޻෉Λ঺հͨ͠ɻ ɾΫϥ΢υίϯϐϡʔςΟϯάʹ·ͭΘΔ՝୊ΛػցֶशΛ͸͡Ίͱ͠ ͨ਺ཧతΞϓϩʔνͰղ͘ݚڀ͕ଟ਺ൃද͞Εͨ͜ͱ͔ΒɺݱࡏऔΓ ૊ΜͰ͍Δݚڀͷൃදͷ৔ͱͯ͠ద੾Ͱ͋Δͱײͨ͡ɻ ɾ͜ͷΑ͏ͳݚڀ͕ଟ਺ൃද͞Ε͍ͯΔʹ΋ؔΘΒͣɺະͩݱ৔Ͱ͸ ϧʔϧϕʔεͷӡ༻͕ͳ͞Ε͍ͯΔ͜ͱ͔Βɺ࣮؀ڥ΁ͷద༻Մೳੑ ʹॏࢹͯ͠ݚڀ͍ͯ͘͠ɻ

31 Early paper submission: March 1, 2021 Notification to early papers: April 15, 2021 Normal paper submission due: May 5, 2021 Final notification to authors: June 17, 2021 Camera-ready paper due: July 1, 2021 དྷ೥ͷ։࠵༧ࠂ: IEEE SERVICES 2021 CHICAGO, ILLINOIS, USA September 5-10 ։࠵༧ఆͷࠃࡍձٞ͸ɺCLOUD/ICWS/SCC/SMDS ͷ4ͭ https://youtu.be/hwkGNGn4mt8 ΑΓҾ༻