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国際会議 IEEE SERVICES/CLOUD 2020 参加報告 / A Report of IEEE SERVICES/CLOUD 2020

国際会議 IEEE SERVICES/CLOUD 2020 参加報告 / A Report of IEEE SERVICES/CLOUD 2020

クラウドコンピューティングを中心とした国際会議IEEE CLOUD 2020に参加してきたので、その報告をまとめています。
https://conferences.computer.org/cloud/2020/program/

Yuuki Tsubouchi (yuuk1)

October 28, 2020
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  1. ࠃࡍձٞ IEEE SERVICES/CLOUD 2020
    ࢀՃใࠂ
    ௶಺ ༎थ@͘͞ΒΠϯλʔωοτ
    2020/10/28

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

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  3. 1.
    IEEE SERVICES/CLOUD 2020ͷ֓ཁ

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  4. 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

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  5. 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

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  6. 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.

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

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  8. 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

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  9. 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

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  10. 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

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  11. 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๏

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  12. 2.
    Best Paper Awardड৆࿦จͷ঺հ

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  13. 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)

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

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  15. 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

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  16. 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੒ͨ͠

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

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

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

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  20. 3.
    ௌߨͨ͠ݚڀൃදͷ঺հ

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  21. 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

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  22. 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

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  23. 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

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  24. 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

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  25. 4.
    ΦϯϥΠϯձ৔ͷ༷ࢠͱ
    ޮՌతͳௌߨͷͨΊͷ޻෉

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  26. 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

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

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

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  29. 5.
    ૯ׅͱདྷ೥ͷ։࠵༧ࠂ

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  30. 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

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  31. 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༻

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