Quan%ta%ve Clustering of Cloud Compu%ng Projects for Be9er Standards Profiling
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1 Quan%ta%ve Clustering of Cloud Compu%ng Projects for Be9er Standards Profiling
2 Authors Neil Caithness Michel Drescher Peter Deussen David Wallom University of Oxford European Grid Infrastructure Fraunhofer FOKUS University of Oxford 2
3 Background We support the European Commission s vision of a digital single market. Standardisa%on is perceived as a strong enabler. We support over 70 EC- funded projects who generally all have standardisa%on and interoperability as an objec%ve. How can we help them? We provide a repeatable methodology for cloud standards profiling. Contribu%ng to the standards landscape: IEEE P2301, ETSI CSC2, ISO/IEC JTC1/SC38 3
4 Our major outputs Set of common standards profiles based on clustering of ini%a%ves on the NIST cloud defini%on characteris%cs. Increasing awareness of security cer%fica%on and legal issues with prac%cal recommenda%ons. Set of prac%cal tools to support cloud adop%on for SMEs and Public Administra%ons. 4
5 Workflow Project Engagement Project selec%on Use- case collec%on Characteris%cs scoring dissemina%on & itera%on Data Analysis Interpreta%on Standards Profiles Principal Components Analysis (PCA) Biplot and numerical representa%on Heirarchical clustering Cluster data quality Func%onal vs. non- func%onal characteris%cs Review and condense project use cases Cloud standards service models Review standards for profiling Quan%ta%ve Methodology 5
6 Defining characteris%cs of cloud compu%ng NIST special Publica%on [NIST ] Essen%al Characteris%cs On- demand self service Broad network access Resource pooling Rapid elas%city Measured service Common Characteris%cs Massive Scale Homogeneity Virtualiza%on Low Cost Somware Resilient Compu%ng Geographic Distribu%on Service Orienta%on Advanced Security 6
7 7
8 PCA Biplot 8
9 PCA Biplot How many components to keep? Scree plot & Kaiser- Gu9man criterion 9
10 Projec%on (heat map) Numerical representa%on of the biplot 10
11 Signal µ Noise σ SNR 11
12 Clustering 12
13
14 14
15 15
16 16
17 17
18 18
19 Workflow dissemina%on & itera%on Project Engagement Data Analysis Interpreta%on Standards Profiles Project selec%on Use- case collec%on Characteris%cs scoring Principal Components Analysis (PCA) Biplot and numerical representa%on Clustering Cluster data quality Func%onal vs. non- func%onal characteris%cs Review and condense project use cases Cloud standards service models Review standards for profiling 19
20 20
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