Tecnologia ed impatto sociale delle GRID Federico Ruggieri

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 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 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 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 coallocation 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, Grid. PP in UK, CHEP 2000 in Padova. 2000 - 2003 First EU Project Data. GRID 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 underevaluation). • 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 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 Problem Several Peta. Bytes (1015 Bytes) of Data every Year

Estimate of Computing needs at CERN for LHC (2000) Non-LHC technology-price curve (40% annual price improvement) ~10 K SI 95 300 processors LHC

Extension of Web Paradigm http: // Web: Uniform Access to Information and Documents Grid: Flexible and High Performance access to (any kind of) resources http: // Software catalogs Sensor nets Computers Colleagues Data Stores On-demand creation of powerful virtual computing and data systems

A Power GRID for Computing

Data. GRID Layered structure (2000) Applications Cosmology Chemistry High Energy Physics Biology Application Toolkits Distributed Computing Toolkit Grid Services (Middleware) Data. Intensive Applications Toolkit Environment Collaborative Applications Toolkit Remote Problem Visualization Solving Applications Toolkit Remote Instrumentation Applications Toolkit Resource-independent and application-independent services authentication, authorization, resource location, resource allocation, events, accounting, remote data access, information, policy, fault detection Grid Fabric Resource-specific implementations of basic services E. g. , Transport protocols, name servers, differentiated services, CPU schedulers, public key (Resources) infrastructure, site accounting, directory service, OS bypass

Natural HEP Farming High Throughput Computing Event #1 CPU 1 Event #2 Event #3 Event #4 CPU 2 Dispatcher CPU 3 Event #5 Event #6 CPU 4

GRID Computing Farm Wide Area Network Computing Element SE CE WN WN Worker Nodes Storage Element

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: X 509 • Creare uno strato software (middleware) per interfacciare i servizi.

Middleware structure Enabling Grids for E-scienc. E Applications Higher-Level Grid Services Workload Management Replica Management Visualization Workflow Grid Economies. . . Foundation Grid Middleware Security model and infrastructure Computing (CE) and Storage Elements (SE) Accounting Information and Monitoring • 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 Overview paper http: //doc. cern. ch//archive/electronic/egee/tr/egee-tr-2006 -001. pdf INFSO-RI-508833

Some history … LHC EGEE Grid Enabling Grids for E-scienc. E • 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 Data. Grid project • • 2001 -2004 - EU Data. Grid project – middleware & testbed for an operational grid 2002 -2005 – LHC Computing Grid – LCG – deploying the results of Data. Grid to provide a production 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 … INFSO-RI-508833 CERN

The release of g. Lite 3. 0 Enabling Grids for E-scienc. E • Convergence of LCG 2. 7. 0 and g. Lite 1. 5. 0 in spring LCG-2 g. Lite 2006 2004 – Continuity on the production infrastructure ensured prototyping usability by applications – Initial focus on the new Job Management prototyping § 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 SA 3 Activity for integration and certification – “Continuous release process” § No big-bang releases! INFSO-RI-508833 product 2005 product 2006 g. Lite 3. 0

Production service Enabling Grids for E-scienc. E sites Size of the infrastructure today: • 192 sites in 40 countries • ~25 000 CPU • ~ 3 PB disk, + tape MSS CPU INFSO-RI-508833

EGEE Resources Enabling Grids for E-scienc. E #countries #sites #cpu Do. W 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 Region * Estimates taken from reporting as IS publishes total MSS space INFSO-RI-508833

(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 UI JDL Replica Input “sandbox” Catalogue Output “sandbox” Input “sandbox” Resource Broker Job Query Job Submit Author. &Authen. Information Service Job Status Storage Element Brokerinfo Job Submission Service Logging & Book-keeping Job Status Output “sandbox” Compute Element

Security & VOMS • GRID usa per la autenticazione degli utenti i certificati digitali (X 509) 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 A • 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.

GRID Security Infrastructure • Based on Public key infrastructure (PKI) • Certification of Personal Identity: a key <=> a user / physical person • PKI: asymmetric encryption • X 509 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. 509 v 3 standard ▪ Certificate ▪ Version ▪ Serial Number ▪ Algorithm ID ▪ Issuer ▪ Validity ▪ 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-init Your identity: /C=IT/O=INFN/OU=Personal/L= Enter GRID pass phrase for this identity: Creating proxy. . . Done Your proxy is valid until: Thu Aug 31 21: 56: 18 2006

Architettura di VOMS Proxy certificate Authentication Holder, Issuer, Validity Certificate extensions User client GSI VOMS 1 signed information Request VOMS 2 signed information User’s attribute s vomsd KCA certificate Auth DB Signature vadmind C=IT/O=INFN User’s attribute /L=CNAF /CN=Pinco Palla s /CN=proxy Auth DB Attributes Group (hierarchically organized) Role (admin, staff, student, . . ) Capability (free-form string) soap Admin client VOMS Server

IS Components Abbreviations: BDII: Berkeley Data. Base Information Index GIIS: Grid Index Information Server GRIS: Grid Resource Information Server Each site can run a BDII. It collects the information coming from the GIISs From LCG 2. 3. 0 site GIIS has been replaced by At each site, “local” a site GIIS BDIIcollects the information given by the GRISs Local GRISes run on CEs and SEs at each site and report dynamic and static information

WMS & JDL • The Workload Management System (WMS) is the g. Lite 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 g. Lite 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).

JDL example • • Type="Job"; Job. Type="Normal"; Executable = “ls"; Std. Error = "stderr. log"; Std. Output = "stdout. log"; Arguments = “-lrt"; Input. Sandbox = "start_hostname. sh"; Output. Sandbox = {"stderr. log", "stdout. log"}; • Retry. Count = 7; • Virtual. Organisation=“ATLAS";

Job Types • Normal • Interactive • MPICH • Partitionable • Checkpointable a simple batch job a job whose standard streams are forwarded to the submitting client a parallel application using MPICH-P 4 implementation of MPI 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 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) • WN is the real execution node • WN is like a Slave User access point to Grid • Proxy credential • Job submission • Job Monitoring • Command info site CE • Entry point of a queue in a batch system • CE is like a Master

Applications Enabling Grids for E-scienc. E • 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 INFSO-RI-508833

ARGO Data Archives e Data Catlog Sync. YBJ: User Interface SRM (DPM) local DB User Interface SRM (DPM) FTS LFC local DB Figure 5 - ARGO Data Transfer Schema

Biology Applications: The “never born” proteins 4 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 ! 4 Does the subset of natural proteins has particular properties? 4 Do exist in principle protein scaffolds with novel structure and/or activity not yet exploited by Nature? 4 GRID technology allows to tackle the problem through high throughput prediction of protein structure of a large library of “never born proteins”

Where grids can help addressing neglected diseases Enabling Grids for E-scienc. E • 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 INFSO-RI-508833

In silico Drug Discovery Enabling Grids for E-scienc. E • Scientific objectives Provide 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. • Method Large 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. INFSO-RI-508833

The virtual screening pipeline Enabling Grids for E-scienc. E Grid service customers DC 1& DC 2 on neglected diseases DC on avian flu Check point Chemist/biologist teams Selected hits Check point hits Grid infrastructure MD service Check point Biology teams target Docking services Annotation services Grid service providers Chimioinformatics teams INFSO-RI-508833 Bioinformatics teams

Collaborating e-infrastructures Enabling Grids for E-scienc. E Potential for linking ~80 countries by 2008 INFSO-RI-508833

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 e. Infrastructures and promote the take-up to speed of other less advanced countries to alleviate as much as possible the so called Digital Divide. • e. Infrastructures 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. • e. Infrastructures 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

First initiative on in silico drug discovery against emerging diseases Enabling Grids for E-scienc. E • Spring 2006: drug design against H 5 N 1 neuraminidase involved in virus propagation – impact of selected point mutations on the efficiency of existing drugs – identification of new potential drugs acting on mutated N 1 H 5 N 1 • Partners: LPC, Fraunhofer SCAI, Academia Sinica of Taiwan, ITB, Unimo University, CMBA, CERN-ARDA, Health. Grid • Grid infrastructures: EGEE, Auvergrid, TWGrid • European projects: EGEE-II, Embrace, Bioinfo. Grid, Share, Simdat INFSO-RI-508833

FP 6− 2004−Infrastructures− 6 -SSA-026024 EUMEDGRID Geography

INCO: International Scientific Cooperation Projects SUSTAINABLE WATER MANAGEMENT IN MEDITERRANEAN COASTAL AQUIFERS: Recharge Assessment and Modelling Issues (SWIMED) ICA 3 -CT 2002 -10004 http: //www. crs 4. it/EIS/SWIMED/menu/index. html • Partnership: • UGR, Spain (Coordinator) • IMFT, France • UNINE, Switzerland • CRS 4, Italy • EMI, Morocco • UG, Palestinian Authority • INAT, Tunisia • UB, Spain

Paramètres hydrodynamiques Altitude du substratum Transmissivités de l’aquifère Manque remarquable de données de transmissivité !!

Archaeo. GRID (1/2) Enabling Grids for E-scienc. E WHERE Archaeological data are geospatial data Archaeological GIS Archaeological sites How many layers ? Number of layers large, depending from the complexity of the archaeological problem Infrastructures Land use Aerial photo or satellite image DEM Best resolution by Differential GPS X, Y, Z ˜ few cm INFSO-RI-508833 Best resolution X, Y < 1 m Z < 0. 1 m (SAR)

Archaeo. GRID (2/2) Enabling Grids for E-scienc. E WHEN Archaeological data must be ordered by time Archaeological GIS + time 1000 B. C. 500 B. C. ~20 years, most precise resolution in time INFSO-RI-508833 500 A. D.

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 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 e. Infrastructures 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.