Tecnologia ed impatto sociale delle GRID

Federico Ruggieri – INFN

Roma “La Sapienza” 18 Ottobre 2006

Indice• Cos’è Grid ?• Dalla Fisica A.E. alle altre Scienze• La Infrastruttura di GRID Europea (EGEE)• Le Applicazioni e le Comunità Scientifiche• Le Aree Geografiche• Impatto Sociale e Digital Divide• Luci ed Ombre• Conclusioni

What GRID is supposed to be

• A dependable infrastructure that can facilitate the usage of distributed resources by many groups of distributed persons or Virtual Organizations.

• The GRID paradigm is an extension of the WEB one, which was originally limited to distributed access to distributed information and documents.

• The classical example is the Power GRID: you plug in and receive power; you don’t know (and you don’t care) where it comes from.

“A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities.” I. Foster & K. Kesselman - The Grid: Blueprint for a New Computing Infrastructure – Morgan Kaufman 1998.

A Grid Checklist• Ian Foster more recently suggested that GRID is a system that:

– 1) coordinates resources that are not subject to centralized control … (A Grid integrates and coordinates resources and users that live within different control domains—for example, the user’s desktop vs. central computing; different administrative units of the same company; or different companies; and addresses the issues of security, policy, payment, membership, and so forth that arise in these settings. Otherwise, we are dealing with a local management system.)

– 2) … using standard, open, general-purpose protocols and interfaces… (A Grid is built from multi-purpose protocols and interfaces that address such fundamental issues as authentication, authorization, resource discovery, and resource access. As I discuss further below, it is important that these protocols and interfaces be standard and open. Otherwise, we are dealing with an applicationspecific system.)

– 3) … to deliver nontrivial qualities of service. (A Grid allows its constituent resources to be used in a coordinated fashion to deliver various qualities of service, relating for example to response time, throughput, availability, and security, and/or co-allocation of multiple resource types to meet complex user demands, so that the utility of the combined system is significantly greater than that of the sum of its parts.)

A bit of (My) short history in Grids

• In the 80’ and early 90’ the accent was on client-server and meta-computing.

• In 1998 I. Foster & K. Kesselman - The Grid: Blueprint for a New Computing Infrastructure & Globus project (www.globus.org).

• First GRID presentation in CHEP’98 – Chicago.• 1999 – 2000 INFN-GRID Project started based on

Globus, GridPP in UK, CHEP2000 in Padova.• 2000 - 2003 First EU Project DataGRID and PPDG &

GRIPHYN in US.• 2003 – 2006 EGEE Project in EU and OSG in US• Many other projects in many countries (Japan,

China, etc.)

A GRID for LHC and HEP• We got involved in Grids to solve the huge LHC

computational problem which was, at that time, starting to be investigated (after an initial under-evaluation).

• In the late 90’ client-server and meta-computing were the frontier and Computer Farms were just started (Beowulf).

• The largest problem anyway was the huge amount of data expected to be produced and analyzed (PB).

• The “social” challenge was to allow thousands of physicists to access those data easily from tens of countries in different continents.

LHC Computational ProblemSeveral PetaBytes (1015 Bytes) of Data every Year

Estimate of Computing needs at CERN for LHC (2000)

Estimated CPU Capacity at CERN

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LHC

Extension of Web Paradigm

On-demand creation of powerful virtual computing and data On-demand creation of powerful virtual computing and data systemssystems

Grid: Flexible and High Performance access to (any kind of)resources

Sensor nets

http://

http://

Web: Uniform Access to Information and Documents

Data Stores

Computers

Softwarecatalogs

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A Power GRID for Computing

DataGRID Layered structure (2000)

Resource-specific implementations of basic servicesE.g., Transport protocols, name servers, differentiated services, CPU schedulers, public keyinfrastructure, site accounting, directory service, OS bypass

Resource-independent and application-independent services authentication, authorization, resource location, resource allocation, events, accounting,

remote data access, information, policy, fault detection

DistributedComputing

Toolkit

Grid Fabric(Resources)

Grid Services(Middleware)

ApplicationToolkits

Data-Intensive

ApplicationsToolkit

CollaborativeApplications

Toolkit

RemoteVisualizationApplications

Toolkit

ProblemSolving

ApplicationsToolkit

RemoteInstrumentation

ApplicationsToolkit

ApplicationsChemistry

Biology

Cosmology

High Energy Physics

Environment

Natural HEP FarmingHigh Throughput Computing

Event #1

Event #2

Event #3

Event #4

Event #5

Event #6

CPU 1

CPU 2

CPU 3

CPU 4

Dispatcher

GRID Computing Farm

WN

WN

WN

WN

CE SE

Wide Area NetworkComputingElement

StorageElement

Worker Nodes

Strategia di base di GRID

•Usare il più possibile ciò che già esiste:–Network: Internet and TCP/IP–Protocols: http, TCP, UDP, ….–Operating Systems: Linux, Solaris, …..–Batch Systems: PBS, LSF, Condor, …..–Storage: Disks, HPSS, HSM, CASTOR, …..–Directory Services: LDAP, ….–Certificates: X509

•Creare uno strato software (middleware) per interfacciare i servizi.

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Middleware structure

• Applications have access both to Higher-level Grid Services and to Foundation Grid Middleware

• Higher-Level Grid Services are supposed to help the users building their computing infrastructure but should not be mandatory

• Foundation Grid Middleware will be deployed on the EGEE infrastructure– Must be complete and robust– Should allow interoperation

with other major grid infrastructures

– Should not assume the use of Higher-Level Grid Services

Foundation Grid Middleware

Security model and infrastructure

Computing (CE) and Storage Elements (SE)

Accounting

Information and Monitoring

Higher-Level Grid Services

Workload Management

Replica Management

Visualization

Workflow

Grid Economies

...

Applications

Overview paper http://doc.cern.ch//archive/electronic/egee/tr/egee-tr-2006-001.pdf

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Some history … LHC EGEE Grid

• 1999 – Monarc Project– Early discussions on how to organise distributed

computing for LHC• 2000 – growing interest in grid technology

– HEP community was the driver in launching the DataGrid project

• 2001-2004 - EU DataGrid project– middleware & testbed for an operational grid

• 2002-2005 – LHC Computing Grid – LCG– deploying the results of DataGrid to provide aproduction facility for LHC experiments

• 2004-2006 – EU EGEE project phase 1– starts from the LCG grid– shared production infrastructure– expanding to other communities and sciences

• 2006-2008 – EU EGEE-II – Building on phase 1– Expanding applications and communities …

• … and in the future – Worldwide grid infrastructure??– Interoperating and co-operating infrastructures?

CERN

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The release of gLite 3.0

• Convergence of LCG 2.7.0 and gLite 1.5.0 in spring 2006– Continuity on the production infrastructure ensured

usability by applications– Initial focus on the new Job Management

Thorough testing and optimization together with the applications

• Migration to the ETICS build system– ETICS project started in January

• Reorganization of the work according to the new process– EGEE Technical Coordination Group and Task

Forces– Start of the EGEE SA3 Activity for integration and

certification– “Continuous release process”

No big-bang releases!

LCG-2

prototyping

prototyping

product

20042004

20052005 product

gLite

20062006 gLite 3.0

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Production service

sites

Size of the infrastructure today:

• 192 sites in 40 countries

• ~25 000 CPU

• ~ 3 PB disk, + tape MSS

CPU

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EGEE Resources

Region #countries #sites #cpu#cpu DoW

disk (TB)

CERN 0 1 4400 1800 770*

UK/I 2 23 4306 2010 310

Italy 1 27 2800 2280 373

France 1 10 2316 1252 300*

De/CH 2 13 2895 1852 280*

Northern Europe 6 16 2379 1860 64

SW Europe 2 13 956 898 16*

SE Europe 8 26 1101 1189 30

Central Europe 7 21 1584 1163 70

Russia 1 15 515 445 38

Asia-Pacific 8 19 840 751 72

North America 2 8 4069 - 229

Totals 40 192 28161 20265 2552

* Estimates taken from reporting as IS publishes total MSS space

(Some) GRID Services• Workload Management System (Resource Broker)

chooses the best resources matching the user requirements.

• Virtual Organization Management System allows to map User Certificates with VO’s describing rights and roles of the users.

• Data Oriented Services: Data & Meta-data Catalogs, Data Mover, Replica Manager, etc.

• Information & Monitoring Services which allow to know which resources and services are available and where.

• Accounting services to extract resource usage level related to users or group of users and VO’s.

Job Submission with Brokering

UIJDL

Logging &Logging &Book-keepingBook-keeping

ResourceResourceBrokerBroker

Output “sandbox”

Input “sandbox”

Job SubmissionJob SubmissionServiceService

StorageStorageElementElement

ComputeComputeElementElement

Brokerinfo

Output “sandbox”

Input “sandbox”

Information Information ServiceService

Job Status

ReplicaReplicaCatalogueCatalogue

Author.&Authen.Job

Subm

it

Job Q

uery

Job Status

Security & VOMS• GRID usa per la autenticazione degli utenti i certificati digitali

(X509) con servizio Globus/GSI ed un sistema di mapping fra questi e gli UID, GID locali dei sistemi.

• GRID si propone di facilitare l’uso di risorse distribuite da parte di comunità virtuali legate ad un tipo di attività, e/o ad esperimenti/progetti.

• E’ perciò necessario un sistema che non solo garantisca AAA (Authentication, Authorization, Accounting), in maniera pressochè uniforme, ma sia in grado di gestire policy di accesso anche complesse.

• Il Virtual Organization Management System cerca di rispondere a queste esigenze facilitando la gestione delle comunità virtuali e le definizioni di ruoli e gruppi di utenti che verranno usate per gestire le policy di accesso alle risorse.

• L’uso di VOMS permette anche ai possessori delle risorse di definire quali comunità (Virtual Organizations) possono accedere alle proprie risorse e con quali limiti.

Public Key Infrastructure

• Based on asymmetric algorithms• Two keys: private key and public key• It is “almost impossible” to derive

private from public.• Data encrypted with one key can be

only decrypted with the other.

A B

GRID Security Infrastructure

• Based on Public key infrastructure (PKI)

• Certification of Personal Identity: a key <=> a user / physical person

• PKI: asymmetric encryption• X509 certificate• Identification of Computers and

Services with PKI Certificates.

X.509 Certificate

• ITU-T standard for PKI• X.509 == IETF PKI cert + CRL of

X.509v3 standard▪ Certificate▪ Version▪ Serial Number▪ Algorithm ID▪ Issuer▪ Validity▪ Subject▪ Subject Public Key Info▪ Public Key Algorithm▪ Subject Public Key▪ Issuer Unique Identifier (Optional)▪ Subject Unique Identifier (Optional)▪ Extensions (Optional)▪ ...▪ Certificate Signature Algorithm▪ Certificate Signature

CA & Proxy• National/Regional Certification Authorities issue Certificates.• Usage of short lived Proxy Certificates to avoid real

Certificates to be archived together with the applications (Delegation).

% grid-proxy-initYour identity: /C=IT/O=INFN/OU=Personal/L=Enter GRID pass phrase for this identity: Creating proxy ............................................. DoneYour proxy is valid until: Thu Aug 31 21:56:18 2006

Architettura di VOMS

C=IT/O=INFN /L=CNAF/CN=Pinco Palla/CN=proxy

User’sattribu

tes AuthDB

Authentication

Request

User’sattribut

es

User client

Admin client

AuthDB

vomsd

vadmindVOMSServer

soap

GSI

AttributesGroup (hierarchically organized)Role (admin, staff, student, ..)Capability (free-form string)

VOMS1 signed information

Holder, Issuer,Validity

Certificate extensions

VOMS2 signed information

KCA certificate

Proxy certificate

Signature

IS Components

Each site can run

a BDII. It collects the information

coming from the GIISs

At each site, a site GIIS collects the information

given by the GRISs

Local GRISes run on CEs and SEs at each site and report dynamic and static information

Abbreviations:

BDII: Berkeley DataBase Information Index

GIIS: Grid Index Information

Server

GRIS: Grid Resource

Information Server

From LCG2.3.0 site GIIS has been replaced by“local” BDII

• The Workload Management System (WMS) is the gLite 3.0 component that allows users to submit jobs, and performs all tasks required to execute them, without exposing the user to the complexity of the Grid.

• The JDL attributes described in this document are the ones supported when the submission to the WMS is performed through the legacy Network Server interface, i.e. using the python command line interface or the C++/Java API of the gLite WMS-UI subsystem . It basically represents a subset of the whole set of attributes supported by the WMS when accessed via the new web services based interface (WMProxy).

WMS & JDL

JDL example• Type="Job";• JobType="Normal"; • Executable = “ls";• StdError = "stderr.log";• StdOutput = "stdout.log";• Arguments = “-lrt";• InputSandbox = "start_hostname.sh";• OutputSandbox = {"stderr.log",

"stdout.log"};• RetryCount = 7;• VirtualOrganisation=“ATLAS";

Job Types• Normal a simple batch job• Interactive a job whose standard streams are

forwarded to the submitting client• MPICH a parallel application using MPICH-P4

implementation of MPI• Partitionable set of independent sub-jobs, each one

taking care of a step or of a sub-set of steps, and which can be executed

in parallel• Checkpointable a job able to save its state, so that the job

execution can be suspended and resumed later, starting from the same point

where it was first stopped.

User Interface (UI)

CE

•Entry point of a queue in a

batch system

•CE is like a Master

User access point to Grid

•Proxy credential

•Job submission

•Job Monitoring

•Command info site

•WN is the real execution node

•WN is like a Slave

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Applications

• Many applications from a growing number of domains– Astrophysics– Computational Chemistry– Earth Sciences– Financial Simulation– Fusion– Geophysics– High Energy Physics– Life Sciences– Multimedia– Material Sciences– …

Applications have moved from testing to routine and daily usage ~80-90% efficiency

0

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YBJ:User InterfaceSRM (DPM)local DB

User InterfaceSRM (DPM)FTSLFClocal DB

Figure 5 - ARGO Data Transfer Schema

Biology Applications: The “never born” proteins

Natural proteins are only a tiny fraction of the possible ones• Approx. 1013 natural proteins vs 20100 possible proteins with a

chain length of 100 amino acids !

Does the subset of natural proteins has particular properties?

Do exist in principle protein scaffolds with novel structure and/or activity not yet exploited by Nature?

GRID technology allows to tackle the problem through high throughput prediction of protein structure of a large library of “never born proteins”

Enabling Grids for E-sciencE

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Where grids can help addressing neglected diseases

• Contribute to the development and deployment of new drugs and vaccines– Improve collection of epidemiological data for research (modeling,

molecular biology)– Improve the deployment of clinical trials on plagued areas– Speed-up drug discovery process (in silico virtual screening)

• Improve disease monitoring– Monitor the impact of policies and programs – Monitor drug delivery and vector control– Improve epidemics warning and monitoring system

• Improve the ability of developing countries to undertake health innovation– Strengthen the integration of life science research laboratories in the

world community – Provide access to resources– Provide access to bioinformatics services

Enabling Grids for E-sciencE

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In silico Drug Discovery

• Scientific objectivesProvide docking information helping in search for new drugs.Biological goal: propose new inhibitors (drug candidates)

addressed to neglected diseases.Bioinformatics goal: in silico virtual screening of drug candidate

DBs.Grid goal : demonstrate to the research communities active in the

area of drug discovery the relevance of grid infrastructures through the deployment of a compute intensive application.

• MethodLarge scale molecular docking on malaria to compute million of potential drugs with some software and parameters settings. Docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm.

Enabling Grids for E-sciencE

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The virtual screening pipeline

Chimioinformatics teams

Biology teams

Docking servicesMD service Annotation services

Bioinformatics teams

target

Chemist/biologist teams

hitsSelected hits

Grid service customers

Grid service providers

Grid infrastructure

Check point

Check point

Check point

DC 1& DC2 on neglected diseases

DC on avian flu

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Collaborating e-infrastructures

Potential for linking ~80 countries by 2008

Social Impact • A large part of the Globe has not advanced digital infrastructures

yet.• The European Research Area program wants to set Europe as the

most advanced region in eInfrastructures and promote the take-up to speed of other less advanced countries to alleviate as much as possible the so called Digital Divide.

• eInfrastructures support wide geographically distributed communities which share problems and resources to work towards common goals -> enhance international collaboration of scientists -> promote collaboration in other fields.

• Problems too big to be handled with conventional local computer clusters and time sharing computing centers can be attacked with GRIDs.

• eInfrastructures are leveraging international network interconnectivity -> High Bandwidth connections will improve exchange of knowledge and be the basis for GRID Infrastructures.

• Based on safe AAA (Authentication, Authorization and Accounting) architecture -> secure and dependable infrastructures.

• Need of persistent software & middleware -> Software is integral part of the infrastructure.

Digital Divide

http://maps.maplecroft.com/

Docking on Malaria

• Of a large interest for many developing countries.• Based on Grid-enabled drug discovery process.• Data challenge proposal never done on a large

scale production infrastructure and for a neglected disease – 5 different structures of the most promising target– Output Data: 16,5 million results, ~10 TB

Enabling Grids for E-sciencE

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First initiative on in silico drug discovery against emerging diseases

• Spring 2006: drug design against H5N1 neuraminidase involved in virus propagation– impact of selected point mutations on the efficiency of existing

drugs – identification of new potential drugs acting on mutated N1

N1H5

•Partners: LPC, Fraunhofer SCAI, Academia Sinica of Taiwan, ITB, Unimo University, CMBA, CERN-ARDA, HealthGrid•Grid infrastructures: EGEE, Auvergrid, TWGrid•European projects: EGEE-II, Embrace, BioinfoGrid, Share, Simdat

FP6−2004−Infrastructures−6-SSA-026024

EUMEDGRID Geography

INCO: International Scientific Cooperation Projects INCO: International Scientific Cooperation Projects

SSUSTAINABLE USTAINABLE WWATER MANAGEMENT ATER MANAGEMENT IINNMEDMEDITERRANEAN COASTAL AQUIFERS:ITERRANEAN COASTAL AQUIFERS:

Recharge Assessment and Modelling IssuesRecharge Assessment and Modelling Issues((SWIMEDSWIMED))

ICA3-CT2002-10004ICA3-CT2002-10004 http://www.crs4.it/EIS/SWIMED/menu/index.html

•Partnership:•UGR, Spain (Coordinator)

•IMFT, France

•UNINE, Switzerland

•CRS4, Italy

•EMI, Morocco

•UG, Palestinian Authority

•INAT, Tunisia

•UB, Spain

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Manque remarquable de données de transmissivité !!Manque remarquable de données de transmissivité !!

Enabling Grids for E-sciencE

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ArchaeoGRID (1/2)

Aerial photo or satellite image

DEM

Land use

Infrastructures

Archaeological sites

WHEREArchaeological data are geospatial data

Archaeological GIS

Best resolution

X,Y < 1 m Z < 0.1 m (SAR)

How many layers ?

Number of layers large, depending from the complexity of the archaeological problem

Best resolutionby Differential GPS

X,Y,Z ˜ few cm

Enabling Grids for E-sciencE

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ArchaeoGRID (2/2)

WHENArchaeological data must be ordered by time

Archaeological GIS + time

1000 B.C.

500 B.C.

500 A.D.

~20 years, most precise resolution in time

Priorities

• The obvious questions are:• Is digital infrastructure a real priority

for them ?• Don’t they have much more urgent,

basic and compelling needs ?• If you have a limited budget, which is

the priority of these investments in respect to more “vital” ones ?

The Advancement chain• It’s obvious that fundamental needs are: food, water, medical

services, etc.• Although they are fundamental in the short term, a long term

solution can’t be build only around those activities of feeding the system.

• A Chinese pillow of wisdom: “if you give a fish to a hungry man you feed him for a while, but if teach him how to fish, you feed him for life.”

• Other activities are necessary to create favorable conditions for a sustainable growth:– Agriculture developments are needed to start producing food and

employment depending on the specific local situation.– Industry will be necessary to start social innovation and an

improvement of the quality of life.– Technology will be the indispensable element which will promote new

products and industrial innovation.– Science is a fundamental component to produce technology and long

term innovation.– Digital Infrastructures are necessary to allow researches to participate

to frontier scientific activities and to be up to speed with the most recent tools and methods.

• So at the root of the activities, with a long term impact, digital infrastructures play an important role.

How to budget• Limiting ourselves to feeding the system will

be an endless investment which will not help to solve the problem in the long term.

• The investment has to be understood and evaluated on several (tens of) years and should have a “figure of merit” respect to the obtained results and the sustainability of future activities.

• All the previous elements of the chain should be investigated and, at different levels of investments, promoted in parallel with different time scales & objectives.

Cosa manca• Le GRID non hanno ancora avuto una completa investitura da

parte del mondo industriale. IBM, SUN, Oracle, ecc. hanno dei prodotti “grid like”, ma siamo lontani ancora dal successo travolgente del web.

• L’infrastruttura (gestione e manutenzione) richiede risorse, principalmente umane, che costano. Si pone il problema della sostenibilità nel lungo termine.

• Le risorse a disposizione sono molte, ma ancora poche sono quelle diverse da sistemi di calcolo (Es. Radio Telescopi, Osservatori e sensori, Grandi Apparati di Fusione, ecc.).

• La Fisica delle A.E. non può ancora passare il testimone ad organizzazioni più larghe che si occupino delle infrastrutture mentre i Fisici tornerebbero ad occuparsi delle applicazioni agli esperimenti.

Conclusions• Grids infrastructure are an expanding reality.• They can stimulate new aggregations of scientists working

together on new challenges which are now made affordable.• They are the basis of eInfrastructures which can promote

high bandwidth networks and make a little step forward to fight Digital Divide in the developing countries.

• But nothing comes for free; you need to know who is using the (your) resources and for which purpose. You need security, accounting and, eventually, billing systems.

• Long Term Sustainability of such a huge investment needs Governmental Priorities and a strong Industrial Uptake.