e-Science CentralJBug

Paul WatsonHugo Hiden

Simon Woodman12th March 2013

Funders: RCUK Digital Economy Programme, Red Hat, Microsoft, EU, TSB

Multi-Disciplinary Projects

DigitalResearch

Cloud Computing• Opportunity to revolutionise IT (and Science)

– Resources on-demand– Scalability– Collaboration

Video

Clinician’sReport

Information for

Users

Methodology section for

papers

Analysis

Sleep Activity Stability Grip … …

Clinician’sReport

Information for

Users

Methodology section for

papers

Analysis

Sleep Activity Stability Grip … …

Cloud

Cloud Computing• Opportunity to revolutionise IT (and Science)

– Scalable resources on-demand– Scalability– Collaboration

• But Major Barriers– Building Cloud-based systems– Security– Governance

Cloud Computing• Opportunity to revolutionise IT (and Science)

– Scalable resources on-demand– Scalability– Collaboration

• But Major Barriers– Building Cloud-based systems– Security– Governance

• Our work to overcome these barriers:

Cloud Infrastructure:Storage & Compute

Ap

p 1

....

Ap

p n

Building on Cloud Infrastructure

Problems

Cloud Infrastructure:Storage & Compute

Ap

p 1

....

Ap

p n

Science requires apps to be:scalable,

reliable, secure

This requires: expertise

time, money,

Cloud Infrastructure: Storage & Compute

Cloud Platform

App 1 .... App n

Cloud Infrastructure:Storage & Compute

Ap

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....

Ap

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App ....

Workflow Enactment

API

Social Networking

Security

Processing Storage

App

Analysis Services

Cloud Infrastructure: Amazon, Azure, Private Clouds

Provenance/Audit

Metadata<expt>9127</expt><smiles>CHC3</smiles>

Cloud Barrier #2: Security

A. Smith378456729

Anonymize Analyzep = 30%q = 27.4r = 34

d0 s1 d2 s3 d4

Patient DataAccelerometer

Data Results

Cloud Federation

Public Cloud

External Services

Internal ITOrganization

Internal Services

Federated Cloud Options

Application

Public Cloud

Private Cloud

Risk?Yes No

Problem

Can’t exploit multiple clouds in one workflow

A. Smith378456729

Anonymize Analyzep = 30%q = 27.4r = 34

d0 s1 d2 s3 d4

Patient DataAccelerometer

Data Results

Partitioning an Application

A. Smith378456729

Anonymize Analyzep = 30%q = 27.4r = 34

Patient DataHeart-rate

Data Results

Public Cloud

Private Cloud

Method(P. Watson, “A Multi-Level Security Model for Partitioning Workflows over Federated Clouds” J. Cloud Computing, Vol. 1(1))

1. Assign Security Level to each Workflow Block2. Check conforms to Bell-LaPadula3. Assign Security Level to each Cloud4. Determine possible allocations of blocks to clouds5. Determine candidate workflow partitioning6. Add inter-cloud data transfers7. Filter8. Apply Cost Model to Rank candidate solutions

1. Assign Security Level to each Workflow Block

A. Smith378456729

Anonymize Analyzep = 30%q = 27.4r = 34

d0 s1 d2 s3 d4

Patient Data

Heart Rate Data Results

Location: 1 0 0 0 0

Clearance: 1 0

3. Assign Security Level to each Cloud

Private Public

C1 C0

Location: 1 0

Extend Bell-LaPadula so a block cannot be deployed on a cloud with a lower security level

pa

d0 s1 d2

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Valid Workflows

8. Apply Cost Model to Rank candidate solutions

Transfer Costs

Workflow Costs #13 4 6 5 2 1

Workflow Costs #21 2 5 6 3 4

Cloud Barrier #3: Loss of Governance

• Barrier to the organisation, not to end-users• “I am very worried about controlling costs, IP

and managing our data assets when so many of our staff are building their own cloud solutions”Director of Major Company, 2012

IT Resources

Governance Policy:Quotas

SustainabilityQoS:

DependabilitySecurity

Performance

Organisation

IT ResourcesOrganisation

Addressing Governance

• Users access the cloud through e-Science Central, not directly

• e-Science Central can enforce governance

Organisation IT Resources

QuotasCostSecurityAudit

Summary

• e-Science Central aims to realise potential of clouds– overcome barriers:

• Scalability, Security, Audit, Governance

• currently supporting £20M research projects• we are very interested in new users and

collaborators– open-source (find us on source-forge)