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From project objectives to Cloud interoperability
Michel Drescher, Cloud Computing Standards Specialist, OeRC
Director, Cloud Consult Ltd.
From project objectives to Cloud interoperability
How to quickly group and classify vast numbers of projects
Recap on the CloudWatch project From project objectives to Cloud interoperability
Quantitative Clustering From clusters to profiles
Rinse & repeat – Adopting a success story 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil 2
THE CLOUDWATCH PROJECT IN 3 SLIDES
Recap of the CloudWatch Project
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil 3
Recap on Objectives
1. Develop CloudWatchHUB.eu promoting European smart cloud services, educating users in understanding cloud computing and ensuring exposure to best practices for cloud standards profiles
2. Raise awareness and education on relevant standards and best practices for interoperability security, privacy, SLAs, reversibility and portability
3. Raise awareness of and promote education about Certification Schemes for cloud services certification
Cloudscape Brazil 2015, Rio de Janeiro, Brazil 4 1 December 2015
Target Audiences – Setting the scene
PRIMARY STAKEHOLDERS SMEs
• Increase adoption potential with online tools
•Providing SMEs a set of resources to carefully plan journey to cloud adoption
Public Authorities (PAs) & Govt.
•Create a dedicated area for PAs and govt. on the CloudWatchHUB.eu
R&I Projects
• Support EC-funded R&I initiatives, in particular DG Connect Unit E2 moving to an asset service oriented approach
SECONDARY STAKEHOLDERS
Standard Development Organisations
(SDOs)
International – European – National –
Regional Funding agencies
Policy Makers
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil 5
QUANTITATIVE CLUSTERING From project objectives to Cloud interoperability – Part 1
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil 7
Lead authors: Neil Caithness & David Wallom, OeRC
8
Project Engagement
• Project selection • Use-case collection • Characteristics
scoring
Data Analysis
• Principal Components Analysis (PCA)
• Biplot and numerical representation
• Hierarchical clustering
Interpretation
• Cluster data quality • Functional vs. non-
functional characteristics
Standards Profiles
• Review and condense project use cases
• Cloud standards service models
• Review standards for profiling
dissemination & iteration
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
Process overview
Process 1: Quantitative Clustering CloudWatch Deliverable D2.4
Process 2: From clusters to Profiles CloudWatch Deliverable D4.3
9
Background
We support the European Commission’s vision of a digital single market. Standardisation is perceived as a strong enabler. We support over 70 EC-funded projects who generally all have standardisation and interoperability as an objective. How can we help them? We provide a repeatable methodology for cloud standards profiling. Contributing to the standards landscape: IEEE P2301, ETSI CSC2, ISO/IEC JTC1/SC38
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
The NIST Cloud Definition Framework
10
Community Cloud
Private Cloud
Public Cloud
Hybrid Clouds Deployment Models
Service Models
Essential Characteristics
Common Characteristics
Software as a Service (SaaS)
Platform as a Service (PaaS)
Infrastructure as a Service (IaaS)
Resource Pooling High Perf Network Access Rapid Elasticity
Measured Service
On Demand Self-Service
Low Cost Software Virtualization Service Orientation
Advanced Security
Homogeneity Massive Scale Resilient Computing
Geographic Distribution
Based upon original chart created by Alex Dowbor - http://ornot.wordpress.com 1 December 2015 Cloudscape Brazil 2015 Rio de
Step 2 – Principal Component Analysis (PCA) & (n-dimensional) biplot
12
• 37 projects • EC and non-
EC supported • Self scoring
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
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Signal μ Noise σ SNR
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
Step 3 – Numerical representation
15
1
2
3
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
Step 4 – Hierarchical clustering
Identified Clusters
Cluster 1 – Scientific computing. This cluster comprises a number of projects that aim at highly distributed data processing in an academic context. Cluster 2 – Trusted public clouds for government. This cluster consists of a set of initiatives driven by public sector organisations. Cluster 3 – High performance, dedicated purpose applications. This cluster is similar to Cluster 1, but comprises projects concentrating on high performance computing that are more focussed regarding their objectives. 16 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
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High performance, dedicated purpose applications
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
Scientific computing
Trusted public clouds for government
FROM CLUSTERS TO PROFILES From project objectives to Cloud interoperability – Part 2
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil 18
From Clusters to Profiles CloudWatch’s methodology
1. Data quality
Avoiding the “rubbish in, rubbish out” problem
2. NIST cloud characteristics Functional vs. non-functional
3. Cloud service models Do projects use the same service model?
4. Standards vs. service models Which service models do standards imply?
5. Review standards for profiling In which way can the selected standards be profiled?
20 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
1. Data quality
Does the data provide information of statistical relevance? Three indicators are examined per cluster: 1. The signal: Agreement Coefficient (AC)
Average value per characteristic over all projects
2. The Noise: Cluster Cohesion (CC) Sample-based standard deviation per characteristic over all projects
3. Signal-to-Noise (SNR) Calculated as AC over CC
SNR values >= 2 indicate good data quality
21 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
2. Functional & Non-functional cloud characteristics
Functional characteristics [E] On-demand self service [E] Broad network access [E] Measured service Virtualisation Resilient computing Geographic distribution Advanced Security
Non-functional characteristics [E] Resource Pooling [E] Rapid elasticity Massive scale Homogeneity Low-cost software Service Orientation
22
Which of the 13 NIST characteristics are most likely in scope for standardisation?
[E] = NIST essential characteristic 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
Service models: IaaS, (IaaS+), PaaS, SaaS Deployment models: Public, Private, Hybrid Cloud characteristics express different on different service & deployment models!
23
3. Projects service & deployment models
Does the data hide further cluster segmentation?
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
4. Which service models do standards address?
Standards addressing IaaS: CDMI, OCCI, OVF TOSCA (complex VM provisioning manifests)
Standards addressing PaaS
TOSCA, CAMP OCCI (via future extensions?)
Standards addressing SaaS: HTML5, JavaScript, HTTP, …
24
This might ask the obvious, but …
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
5. How can candidate standards be profiled?
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Compliance does not guarantee interoperability! Which compliance requirements can be scoped down further?
The standards profiler’s toolkit Notes – clarify ambiguous (non-)normative text Restrictions – scope down alternatives and options
MAY, MAY NOT, etc. MUST, MUST NOT, etc. Extensions – define the undefined
Forbid extension points where required Define (exhaustively!) allowed extensions
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
Cluster 1 – scientific computing
Most important characteristics On demand self service – Massive Scale – Homogeneity
Strawman profile standards OCCI/CIMI & CDMI
26 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
Cluster 2 – Trusted public clouds for governments
Most important characteristics Measured service – Advanced security – Resource pooling
Strawman profile standards Usage Record 2, NIST SP 500-307, CIMI, (AMQP) ISO/IEC 27000, NIST SP 800-53, CCM 3.01
27 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
Cluster 3 – High performance dedicated purpose applications
Most important characteristics Geographic distribution – Massive Scale – Measured Service – Massive Scale
Strawman profile standards OCCI Usage Record 2, NIST SP 500-307, CIMI, (AMQP)
28 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil
RINSE & REPEAT – ADOPTING A SUCCESS STORY
Some experience to share with EUBrazil Cloud Connect andbeyond
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil 29
Conclusions Through identification of cluster members;
Support cross cluster communication Identification of key issues Allows providers to identify targeted offerings
Through clustering methodology Quickly assemble a large data foundation Individual positioning in cluster landscape
Through qualitative analysis of cluster data Support delivery to SDO areas of future activities Initiate and facilitate focused technical discussions based on facts
EC project ‘Clusters’ have the opportunity to use clustering technique to ensure there is internal alignment of projects
1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil 30
Next Steps Collaboration with IEEE P2301
Expand the benchmark Improve the cloud definition encapsulated by NIST
Follow-up with identified clusters
Informal participation established at 6th CloudWatch standards workshop
Rinse and repeat!
Export the methodology worldwide
32 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil