Технологические вызовы науки “больших данных” иЕвропейские проекты в области e-Science
Big Data Science Challenges and European e-Science
Projects

Yuri Demchenko,
SNE Group, University of Amsterdam
TiPVSIT2012
13-20 August 2012, Ulan-Ude, Russia

Outline•Наука “больших данных” (Big Data Science) как следующая технологическая
волна
–

•

Категории данных и жизненный цикл данных в e-Science(Data categories and Data Lifecycle in modern e-Science)Европейская политика в области научной и технологической информации (НТИ)(EC policy on Scientific and Technology Information (STI))––Публичная научная информация и Открытый Доступ
(Public Scientific Information and Open Access)Horizon 2020 Consultation meeting in Rome 11-12 April 2012•Инфраструктура научных данных по отраслям, проекты и инициативы в Европе
(Scientific Data Infrastructures (SDI) by scientific communities, projects and initiatives
in Europe)•Архитектура Инфраструктуры научных данных
(Defining SDI architecture)– Облачные технологии как инфраструктурная база для сложных.больших данных
Clouds as an infrastructure platform for complex/scientific data
– (Дополнительно) Построение доверительной инфраструктуры для науки
(Defining Trustworthy platform fro SDI)•

Необходимость новой образовательной специальности
(Need for new scientific and academic discipline)

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System and Network Engineering (SNE) Group at
the University of Amsterdam••

SNE group is primarily a research group but also supports SNE master education
Main research areas– High speed optical networks• Recent testbed achieved sub-40Gbps at Amsterdam-CERN link

– Information modeling for network and infrastructure services description– Security and generic AAA Authorisation framework (GAAA-AuthZ)• Evolving from client/security model to dynamically provisioned services•

New research areas– Cloud and Inter-Cloud Architecture for interoperability and integration– Infrastructure and security issues in Big Data Science and Scientific Data Infrastructure

•
•
•
•

Long term research cooperation with SURFnet and GigaPort programs in NL
Recent and current projects participation – DatGrid, NextGrid, EGEE,
Phosphorus, GEYSERS, GEANT3, NOVI, AAA Study
Active contribution to standardisation activity at OGF, IETF, IEEE, TMF, NIST andothers
Maintaining own optical networking testbed and cloud IaaS and PaaS testbeds

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Big Data Science as the next technology focus• Big Data is becoming the next buzz word
• Based on entire information and artifacts digitising– Requires also new information and semantic models for information
structuring and presentation– Requires new research methods using large data sets and data mining• Methods to evolve and results to be improved• Changes the way how the modern research is done (e-Science)– Secondary research, data re-focusing, linking data and publications• Big Data require infrastructure both to support distributed data(collection, storage, processing) and metadata/discovery services– Demand for trusted/trustworthy infrastructure– Clouds provide just right technology for (data supporting) infrastructure
virtualisation

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Horizon2020 Consultation Meeting(Rome 11-12 April 2012)•

Vice-President of the European Commission Mme Neelie Kroes– "Open e-Infrastructures for Open Science" - The Digital Agenda and Access to Scientific
Information•

Working Group 1: Open Global Data Infrastructure: towards an internationalframework for collaborative scientific data infrastructure– Several contributing "position papers" produced about the subject of a "Data Web Forum" ora "Data Access and Interoperability Task Force" and reports such as Riding the Wave fromthe High-Level Group on Scientific Data or the report of the G8+5 working group on Data.

••

Working Group 2: Open Scientific Content: e-Infrastructure policies and services
to support access, storage, processing and exchange of scientific information
Working Group 3: Open Research Culture: Engagement of researchers andsociety with Open Science through collaborative data infrastructure.

Declaration from Rome meeting“Researchers and practitioners from any discipline are able to find, access and process the data theyneed in a timely manner. They are confident in their ability to use and understand data, and they canevaluate the degree to which that data can be trusted.
Data are stored, managed, shared, and preserved in a way that optimizes scientific discovery,
innovation, and societal benefit. Where appropriate, producers of data benefit from opening it to broad
access and routinely deposit their data in reliable repositories. A framework of repositories work tointernational standards, to ensure they are trustworthy.”

Next EU Framework Program FP 8 Horizon212 2013-2020
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Scientific Research and Scientific Data
Scientific method is a body of techniques for investigating
phenomena, acquiring new knowledge, or correcting andintegrating previous knowledge• Two basic research methods– Observation (passive, selective, active)– Experiment (on existing object, with object re-creation)• Scientific instruments and Measurements– Measurements as a basic instrumental method and building block ofscience– Science begins when it can measure and record data

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Scientific Data Model/Hierarchy

Publications
with Data

Published Data

Structure Data/Datasets

Raw Data

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Traditional Data Lifecycle Model
Traditional Data Lifecycle Model
Project/
Experiment
Planning

Data collection

Data
Integration andprocessing

Publishingresearch
results

Discussion/
feedback

Archivingor
Discarding

User

•
•
•
•
•

Data collection
Data processing
Publishing research results
Discussion
Data and publications
archiving
Lack of initial data preservationand data linking to publications
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Data Lifecycle Model in e-Science - II
Data Lifecycle Model in e-Science

User
Researcher

Data discovery

Data Curation(including retirement and clean up)
Data
recycling

Raw Data
Experimental
Data

Project/
Experiment
Planning

Data collection
andfiltering

Structured
Scientific
Data

Data analysis

DB

Data archiving

Data
Re-purpose

Data linkage
to papers

Data sharing/
Data publishing

Data Re-purpose

Data Linkage Issues•
Persistent Identifiers (PID)•
ORCID (Open Researcher andContributor ID)•
Lined Data

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Open
PublicUse

Data Clean up and Retirement•
Ownership and authority•
Data Detainment

Big Data Science SDI

Data
archiving

Data Links

Metadata &
Mngnt

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Big Data Science (1)• High Energy Physics (HEP)– Running experiment LHC and infrastructure WLCG
• Already producing PBytes of information• Worldwide distribution and processing– CERN and national HEP centers– Projects: WLCG (former EGEE), EGI (European Grid Infrastructure)• Low Energy Physics and Material Science (photon, proton, laser,
spectrometry)– Number of research facilities serving international communities– Multiple short projects producing TBytes of information• Experimental data storage, identification, trusted access to multiple users (including public andprivate researchers)– Projects: ELIXIR and many small projects• Earth, weather and space observation– Climate research and Earth observation• With new 4? satellites to be launched starting 2017 to produce PBytes monthly– ESA (European Space Agency)– Projects: Helix Nebula

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Big Data Science (2)• Life science and biodiversity (Genomic, Biomedical and
Healthcare research)– Human genome (EMBL-EBI)• Currently centralised databases but evolving to distributed• ELSI data - Special requirements to data integrity and privacy– Numerous local/offline databases to be brought online– Living species and biodiversity• Mobile/field access, filtering and on-demand computing• Public contribution, vocational or citizen researchers– Projects: ELIXIR, EGA, LifeWatch• Humanities (History, languages, human behaviour)– Rediscovering research with total information digitising• Expected huge amount of data to digitise all human heritage– Very spread research community– Projects: CLARIN, DARIAH, EUDAT• Additional: Data collection from sensors and online activities– Intelligence, Homeland Security, Log data (+ Facebook data :-)13-20 August 2012, Ulan-Ude,
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Big Data Challenges and Initiatives• Peta and Exa scale problems– Storage, Computing, Transfer/Network– International Exascale Software Project (http://www.exascale.org/)– A Vision for Global Research Data Infrastructure(http://www.grdi2020.eu/)• G8+O5 Global Research Data Infrastructure, Subgroup on Data, Draft
Report, 28 October 2011

• Data Web Forum (DWF)– Initiated by NSF (Alan Blatetsky)– Launch is planned for autumn 2012 NSF event• DAITF – Data Access and Interoperability Task Force– Initiated by EUDAT project (in association with CLARIN)• Primarily not by e-Science/Grid community– Concertation meeting – Sept 2011, Lyon
• Trying to follow IETF format
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Coordination in European Research Area (ERA)• European Commission – but not only
• EIROforum – European Intergovernmental Research Organisation– Profile committees organised by scientific domain• ESFRI – European Strategy Forum for Research Infrastructure– Coordinates projects and funding for Research Infrastructures (RI) (20022010)• eIRG – e-Infrastructure Reflection Group– High level policy development for Europe on e-Infrastructure• EEF - European e-Infrastructure Forum– Principles and practices to create synergies for distributed Infrastructures
CERN - High Energy Physics and LHC experiment•• TERENA– REFEDS – Research and Education Federations• LIBER – Association of European libraries– Growing role of scientific libraries including access to research information
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Existing and emerging SDI• WLCG – Worldwide LHC Grid (CERN, Geneva)• EGI – European Grid Infrastructure– Operational Grid infrastructure serving around 10,000 researches worldwide– Published “Seeking new horizons: EGI’s role for 2020”• HELIX Nebula – The Science Cloud (prospective cloud based
SDI)– Private partnership project with limited EC support• Growing RI for different research communities– CLARIN, EUDAT, LifeWatch, ELIXIR, etc.
• Less technology and more subject focused

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EC Strategy and Policy on STI (Scientific andTechnology Information)• Covers 2 main areas– Scientific Information policies defining priorities for STI in Europe andharmonise national policies– e-Infrastructure for scientific information• Management of scientific data during their whole lifecycle: includes creation,access, (re)use, and preservation• Main targets– New technologies for research and scientific data use– Better access to support information exchange and cooperation– Preservation of data for future reuse and secondary research• EC initiative on Open Access scientific publications from publicly
funded projects• G8+O5 Global Research Data Infrastructure, Subgroup on Data,
Draft Report, 28 October 2011
– Requires “reliable infrastructures for persistent identification of data (e.g.
digital object identifiers, handle systems), researchers (e.g. digital
authors identifiers), and authentication, authorization and accounting
systems (AAA)”
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Open Access to Scientific Publications• EC initiative on Open Access scientific publications frompublicly funded projects– Included into Declaration from Rome meeting– Approx 3500 publicly funded ROs and 2000 privately funded ROs– Special funding scheme for reimbursing publications– Ongoing consultations with China, India, Russia compliance to OA
principles at high governmental level• OpenAIRE project exploring models for open access topublications– PID (personal researcher identifier), ORCHID (Open Researcher andContributor ID), Linked data

• Community initiative - Panton Principles for Open Data in
Science (http://pantonprinciples.org/)
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SDI and AAA related Issues for SDI/Big Data
1. Communities and their specific Big Data challenges
2. Peta and Exa scale in Computing, Storage, Communications
3. Data lifecycle management
4. Data provenance
5. Data linking to publications and PID/ORCHID
6. Trusted/trustworthy SDI and trust management
7. Data protection and privacy
8. Data usage accounting
9. Embedded access policy
10. Longterm preservation and related access control issues
11. Federated access control and data centric access control
12. SDI security, trustworthiness and Clouds
13. Future AAI/AAA for SDI and evolution
14. SDI and AAI architecture, models, mechanisms
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Architecture framework for SDI and ACAI/AAI• e-SDI multi-layer architecture model• Data lifecycle model• RORA model to define relationship between
resources and actors– Potentially new actor in SDI – Subject of data (e.g.
patient, or scientific object/paper)• Access Control and Accounting Infrastructure– Authentication, Authorisation, Accounting• Supported by logging service– Extended to support data access control and operations
on data
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SDI Architecture Model

Layer B6
Scientific
Applications

ScientificDatasetApplicUser
portals

Scientific
ApplicScientific
ApplicScientificUser/Scientific Applications Layer

Metadata and Lifecycle Management

Security and AAI

Operation Support and Management Service (OSMS)

Layers

Policy and Collobaritive Groups Support

Shared Scientific Platform and Instruments(specific for scientific area)

Cloud/Grid Infrastructure
Virtualisation and Management
Middleware

Middleware
security

ComputeResources

StorageResources

Network infrastructure

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Layer B5
Federation Layer(Policy and User
Group Support)
Layer B4
Scientific Platform
and Instruments
Layer B3
Infrastructure
Virtualisation

Layer B2
Datacenter andComputer Facility
Layer B1
Network Infrastructure

Technologies andsolutions
Scientific specialist
applications
Library resources

eduGAIN, RFEDS,
VOMS, InCommon

PRACE/DEISA

Grid/Cloud

Cloudeduroam

19

SDI move to Clouds• Cloud technologies allow for infrastructure
virtualisation and its profiling for Data structures• Clouds provide just right technology for infrastructure
virtualisation to support data sets

• Cloud can provide infrastructure on-demand tosupport scientific workflows– Similar to Grid but with benefits of the full infrastructure
provisioning on-demand– Including support for dynamically provisioned Virtual
Organisations or associations

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General use case for infrastructure provisioning:
Workflow => Logical (Cloud) InfrastructureEnterprise/Scientific workflow
Storage
Data

SpecialProc 1
Data
Filtering

Input
Data

VisualPresent
SpecialProc 2

Instrum.
Data

Campus A

Data
Archive

Visualisation

Visualisation

CE

User
Group A

Campus B

CE

User
User
User

User
User
User
VR6

Cloud 2 PaaS

VR2

User
Group B

VR7
VR4

VR1

VR5

Resource/
Service
ProviderVR3Enterprise/Project basedIntercloud Infrastructure

Cloud 1 IaaS

Resource/
Service
Provider
CN

CN
CN

CN
CN

CN

Cloud PaaS Provider

CN
CN

CN

CN
CN

Cloud IaaS Provider

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General use case for infrastructure provisioning:
Workflow => Logical (Cloud) InfrastructureEnterprise/Scientific workflow
Storage
Data

SpecialProc 1
Data
Filtering

Input
Data

VisualPresent
SpecialProc 2

Instrum.
Data

Campus A

Data
Archive

Visualisation

Visualisation

CE

User
Group A

Campus B

CE

User
User
User

User
User
User
VR6

Cloud 2 PaaS

VR2

User
Group B

VR7
VR4

VR1

VR5

Resource/
Service
ProviderVR3Enterprise/Project basedIntercloud Infrastructure

Cloud 1 IaaS

Resource/
Service
Provider
CN

CN
CN

CN
CN

CN

Cloud PaaS Provider

CN
CN

CN

CN
CN

Cloud IaaS Provider

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General use case for infrastructure provisioning:
Logical Infrastructure => Network Infrastructure (1)Resource and Cloud Provider Domains
Cloud 1 IaaS
VR3

VR1

Cloud 2 PaaS

VR5

VR7

Campus A
Infrastructure

Campus B
Infrastructure

VR2

Campus A

VR4

VR6

Cloud Carrier Network Infrastructure

Visualisation

Visualisation

CE

User
Group A

Campus B

CE

User
User
User

User
User
User
VR6

Cloud 2 PaaS

VR2

User
Group B

VR7
VR4

VR1

VR5

Resource/
Service
ProviderVR3Enterprise/Project basedIntercloud Infrastructure

Cloud 1 IaaS

Resource/
Service
Provider
CN

CN
CN

CN
CN

CN

Cloud PaaS Provider

CN
CN

CN

CN
CN

Cloud IaaS Provider

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New Scientific and Academic Discipline• New Scientific and Academic Discipline is needed
for Big Data Science– Included into declaration of the Rome meeting– One of the priority item for the next FP8 program
Horizon2012 (2013-2020)• To address– ICT infrastructure building, operation and optimisation– Big data and distributed computing– Metadata and semantics– Security and trustworthiness Infrastructure and Data

• Need to educate/train new specialists in Big Data
– Primarily for not ICT savvy community
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Trustworthy SDI – Concept and Foundation• Trust model for SDI and ICT• Components of trustworthy ICT• Security in virtualised environment

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Trust model – Transposition from real world(human practice) to ICT domain••

Trust is two or multi-party relational concept
Common trust definition and presented in [1] primarily taken from life
A trusts B to do something (typically Action, Operation, or Assertion)••

This definition is suitable for simple trust models like in PKI, authentication,authorisation or delegation
However transposition of this definition to more complex cases as intended
Trustworthy infrastructure require more factors to be included– Two or more parties involved into trust relations, typically Users (or user client/application)
and Provider (or server or service/application)– Data with their ownership, privacy, confidentiality level, and applied/attached handling policy– Computing platform and storage (which however can associated with the general platform)– Underlying communication infrastructure (which however can associated with the generalplatform)– Possible interdomain issues related to different policies and assurance/provisions
User A trusts service B to do an operation O (or make Assertion) with data D on platform P

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Trust model – Transposition to ICT/SDI domain
Classic Trust definition (“humanised”)
A trusts B to do something(typically Action, Operation, or Assertion)
Trust definition transposed to Trustworthy ICTUser A trusts service B to do an operation OPwith data D on platform P• Data are associated with the handling policy• Platform is multi-domain and multi-provider• Platform includes Computing, Storage and Network• Data may be stored by 3rd party and belong to domain different touser
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Component of the Trusted/Trustworthy ICTUser Trust domain A
Data

Trust between
domains A and B

Provider Trust domain B

Data stored atuser facilities

Data

Application(Operation)

User

User Client/
SystemData stored
in Provider domain

Platform/
SystemData

ProviderUser Client
trusts provider’s
System/Platform

Trust(worthiness)
Security

User and Service Provider – two actors concerned with own Data/Content security andeach other System/Platform trustworthiness
Data may be stored separately but typically belong to user trust domain•

To be extended to multidomain and distributed environment

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Security in Virtualised Environment:
Platform, OS/VM, Application and Network
Platform –OS/VM – Container – Application
Multilayer security

Platform –OS/VM – Container – Application
Multilayer security

Virtualisation Platform (Cloud)Virtualisation Platform (Cloud)VPN basedtransport network security

OS/VM
Container
ApplInter-container TLS/SSL security

OS/VM
Container
ApplInter-applications e2e security

Metadata
Information/
Resolution
Service

Session Credentials/Context
User/System Credentials, secrets
Platform trust anchor (TPM, hardware secret key)•••

Explanation about multilayer security and trust
TODO: Add Data related components: metadata, encapsulated policies
TODO: Add policies at each layer

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Discussion - Обсуждение
Вопросы - Questions

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Additional and backdground materials• LIBER Publications Pyramid• DAITF model• Distributed Computing Fallacy

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Data Publication Pyramid (Credits LIBER)

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Distributed Computing Fallacy
Classical definition
1. The network is reliable
2. Latency is zero
3. Bandwidth is infinite
4. Transport cost is zero
5. The network is secure
6. Topology doesn't change
7. There is one administrator
8. The network is homogeneous

New aspects brought by clouds and virtualisation
9. Network connection setup time is zero10.

http://www.infoq.com/news/2009/05/fallacies-distributed-computing
http://en.wikipedia.org/wiki/Fallacies_of_Distributed_Computing
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AAI/AAA for SDI and Big Data
• AAI for SDI requirement• Traditional AAI• AAI architecture model for AAI

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AAI Architecture model for e-SDI - Requirements• Federated access and identity management– Different access devices and media• Multi-level access control to distributed information resources– Distributed inter-linked data and supporting services/information• Controllable access to datasets and data
– Data lifecycle stages– Long term preservation and linking policy to data
– Linking papers and data

• Capable to protect data at Peta- and Exascale• Data use accounting– At least Who/Subject accessed and What/Action did?

• Trusted platform and virtualised/Cloud environment
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AAI Architecture Model - Traditional
PolicyAuthority

PDP

PermitAct

User AuthN Creds

User/Reqstr(Client/
Browser)

AuthZ
PEP

AuthN

User DB

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Context

Attribute
Authority

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AAI Architecture Model for Data Centric SDI•

AAI for e-SDI incorporates a number of functionalities to support
infrastructure virtualisation and on-demand provisioning–

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IETF standardisation on AAI• IETF abfab-wg (Application Bridging for Federated
Access Beyond web)
– Initiative and contribution from Moonshot Project (JANET and
GEANT3)– Extends web-based federated access (authentication andauthorisation) to inter-applications and inter-layers• OAuth2.0– Initially proposed and used by Facebook, now community contribution• OpenID - Enables an End-user to communicate witha Relying party (RP) to verify the end-user's identifier– Widely supported by NREN and research community• IETF kitten-wg – Kerberos and SASL update and extension
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OGF Standardisation on AAI in Grids and Clouds• Grid AAI related standards– Basic Authentication profile– GIN-WG (Grid Interoperability Now) AAI related standards– Grid Authorisation and VOMS X.509 Attribute Certificate– XACML-Grid profile (draft version)• Cloud interoperability and federation– Federated access control to Cloud resources

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User Access and Data Security in Clouds• Security challenges in clouds• Cloud environment and problems to be addressed• Emerging cloud Security models

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Cloud Computing Security – Challenges• Fundamental security challenges and main user concerns in
Clouds– Data security: Where are my data? Are they protected? What control
has Cloud provider over data security and location?
– Identity management and access control: Who has access to mydata?

• Two main tasks in making Cloud secure and trustworthy– Secure operation of Cloud (provider) infrastructure– Provide user controlled access control (security) infrastructure• Provide sufficient amount of security controls for user

• Cloud security infrastructure should provide a framework for
dynamically provisioned Cloud security services andinfrastructure
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Cloud Environment and Problems to be addressed
•
•
•
•
•

Virtualised services
On-demand/dynamic provisioning
Multi-tenant/multi-user
Multi-domain
Uncontrolled execution and data storage environment– Data protection• Trusted Computing Platform Architecture (TCPA)• Promising homomorphic/elastic encryption (to be researched)• Integration with customer legacy security
services/infrastructure• Integration with the providers business workflow

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Emerging Cloud Security Models• Former (legacy): Provider - User/Customer• New Cloud oriented security provisioning models– Provider - Customer - User
• Enterprise as a Customer, and employees as Users• Enterprise/campus infrastructure and legacy services– Provider – Operator (Broker) - Customer – User
• Application area IT/telecom company serves as an Operator for
application services infrastructure created for customer company• Security issues/problems in new security provisioning
models– Integration of the customer and provider security services– Identity Management and Single Sign On (SSO)• Identity provisioning for dynamically created Cloud based infrastructure orapplications
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