From project objectives to Cloud interoperabilityeubrazilcloudconnect.eu/sites/default/files/CloudScape Brazil 2015... · From project objectives to Cloud interoperability Michel

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


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Today’s talk

1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil

QUANTITATIVE CLUSTERING From project objectives to Cloud interoperability – Part 1


Lead authors: Neil Caithness & David Wallom, OeRC

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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


Process overview

Process 1: Quantitative Clustering CloudWatch Deliverable D2.4

Process 2: From clusters to Profiles CloudWatch Deliverable D4.3

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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


The NIST Cloud Definition Framework

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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

11 1 December 2015 Cloudscape Brazil 2015, Rio de Janeiro, Brazil

Step 1 – Characteristics scoring

Step 2 – Principal Component Analysis (PCA) & (n-dimensional) biplot

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• 37 projects • EC and non-

EC supported • Self scoring


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Signal μ Noise σ SNR


Step 3 – Numerical representation

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1

2

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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


Scientific computing

Trusted public clouds for government

FROM CLUSTERS TO PROFILES From project objectives to Cloud interoperability – Part 2


Picking up the data


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?


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


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

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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!

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3. Projects service & deployment models

Does the data hide further cluster segmentation?


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, …

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This might ask the obvious, but …


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


Cluster 1 – scientific computing

Most important characteristics On demand self service – Massive Scale – Homogeneity

Strawman profile standards OCCI/CIMI & CDMI


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


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)


RINSE & REPEAT – ADOPTING A SUCCESS STORY

Some experience to share with EUBrazil Cloud Connect andbeyond


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


CloudWATCH2 2015 - 17


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


Thank you!


Documents

From project objectives to Cloud interoperabilityeubrazilcloudconnect.eu/sites/default/files/CloudScape Brazil 2015... · From project objectives to Cloud interoperability Michel