Ultra-Large-Scale Systems Seven Years after the Study - ICSE 2013 ...

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CPS/ULS FY12Technical Results (CuTechnical problem: XSEDE needs well-defined software developmentand software management practices across the XSEDE partner networkbefore it can embrace practices more appropriate for a socio-technicalecosystem.Approach:• Identify and coach XSEDE community in adopting a variant ofarchitecture-centric practices suitable for their collaborative ecosystem• Apply automated text and social network analyses to data gatheredfrom XSEDE with the goal of providing automated infrastructure supportfor ensuring that the right people get the right information at the righttime.Impact: Enabling the evolution of the nation’s scientific computing grid viaarchitecture-centered practices and serving as an exemplar for sociotechnicalecosystems.Does Scale Really Matter?: ULS Systems Seven Years LaterLinda Northrop: May 24, 2013© 2013 Carnegie Mellon University54
Healthcare AnalyticsProblem: Define healthcare analytics from technical andorganizational perspectives needed to achieve intelligent healthcare.Healthcare InfrastructureHealth IT as an UltraLarge-Scale SystemApproach: Define an analytics framework.Does Scale Really Matter?: ULS Systems Seven Years LaterLinda Northrop: May 24, 201355© 2013 Carnegie Mellon University
Page 2 and 3:
Software Engineering Institute (SEI
Page 4 and 5: Ultra-Large-Scale (ULS)SystemsDoes
Page 6 and 7: Beginning of theULS System JourneyD
Page 8 and 9: Societal ProblemsClimate change and
Page 10 and 11: Trend Toward Increasing Scale-1•
Page 12 and 13: Increasing Scale In Military System
Page 14 and 15: Expert PanelGregory AbowdGeorgia In
Page 16 and 17: Inspiration: Open Source and Cooper
Page 18 and 19: ULS Systems Research Study Reportht
Page 20 and 21: What Is an Ultra-Large-Scale (ULS)
Page 22 and 23: Approaches to Software DevelopmentT
Page 24 and 25: Analogies are UsefulDoes Scale Real
Page 26 and 27: Think EcosystemDiverse users with c
Page 28 and 29: ChallengesULS systems will present
Page 30 and 31: What We LearnedThere is an unstoppa
Page 32 and 33: Early Post-Study Observations• We
Page 34 and 35: Since ThenDemonstrators in Cairo's
Page 36 and 37: Software is Ubiquitous and Often Tr
Page 38 and 39: More Fuel for ScaleDoes Scale Reall
Page 40 and 41: The “Crowd”DARPA BAA 11-64: Soc
Page 42 and 43: Some ULS Systems Buzz~21,266 downlo
Page 44: Upon ReflectionSTE(Social-Technical
Page 47 and 48: Selected Experiences withSystems at
Page 49 and 50: Smart Grid - A ULS SystemDiagrams c
Page 51 and 52: Broader ULS System ImpactThe expans
Page 53: XSEDE’s innovative, open standard
Page 57 and 58: Adding AnalyticsBusiness andMission
Page 59 and 60: Best in Class Healthcare AnalyticsO
Page 61 and 62: Sample Published WorkContextual Des
Page 63 and 64: Selected SEI ResearchTargeted at UL
Page 65 and 66: Architecture in ULS System ContextU
Page 67 and 68: SEI and Broader Carnegie Mellon Col
Page 69 and 70: So, Where Are We?The report has bee
Page 71 and 72: Opportunities and ThreatsOpportunit
Page 73 and 74: Putting Technology to Work: a Few T
Page 75 and 76: What I SeeMachine LearningMulticore
Page 77 and 78: Food for Thought• Is our research
Page 79 and 80: Thanks To The Entire ULS System Stu
Page 81 and 82: A Special Thank You and TributeDoes
Page 83 and 84: InteractionDoes Scale Really Matter
Page 85 and 86: References for Slides 65-66Does Sca
Page 87 and 88: References - 2Bryant, Barrett R., J
Page 89 and 90: References - 4Clements, Paul, and M
Page 91 and 92: References - 6Froihofer, Lorenz, Ge
Page 93 and 94: References - 8Hill, James H., Jules
Page 95 and 96: References - 10Lucrédio, Daniel, E
Page 97 and 98: References - 12Salehie, Mazeiar, an
Page 99 and 100: References - 14Valerdi, Ricardo, El
Page 101 and 102: References - 16Zhu, Liming, and Yan
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