Download GRID: from HEP to society

Tecnologia ed impatto
sociale delle GRID
Federico Ruggieri – INFN
Roma “La Sapienza” 18 Ottobre 2006
Indice
•
•
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•
•
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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, 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 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 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 Problem
Several PetaBytes (1015 Bytes) of Data every Year
Estimate of Computing needs
at CERN for LHC (2000)
Estimated CPU Capacity at CERN
2,500
2,000
K SI95
Non-LHC
1,500
technology-price curve (40%
annual price improvement)
1,000
~10K SI95
300 processors
LHC
500
0
1998
1999
2000
2001
2002
year
2003
2004
2005
2006
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
DataGRID Layered structure
(2000)
Applications
High Energy Physics
Biology
Application
Toolkits
Distributed
Computing
Toolkit
Grid Services
(Middleware)
Environment
Cosmology
Chemistry
DataIntensive
Applications
Toolkit
Collaborative
Applications
Toolkit
Remote
Visualization
Applications
Toolkit
Problem
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
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: X509
•Creare uno strato software (middleware)
per interfacciare i servizi.
Middleware structure
Enabling Grids for E-sciencE
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-sciencE
•
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 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 gLite 3.0
Enabling Grids for E-sciencE
• Convergence of LCG 2.7.0 and gLite 1.5.0 in
spring 2006
LCG-2
2004
– Continuity on the production infrastructure ensured
usability by applications
– Initial focus on the new Job Management
gLite
prototyping
prototyping
 Thorough testing and optimization together with the
applications
product
• 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!
INFSO-RI-508833
2005
product
2006 gLite 3.0
Production service
Sites
Enabling Grids for E-sciencE
200
180
160
140
120
100
80
sites
60
40
20
ec
-0
5
D
ct
-0
5
O
Au
g05
Ju
n05
Fe
b05
Ap
r05
ec
-0
4
D
ct
-0
4
O
Au
g04
Ju
n04
Ap
r-
04
0
Size of the infrastructure today:
• 192 sites in 40 countries
• ~25 000 CPU
• ~ 3 PB disk, + tape MSS
30000
No. CPU
25000
20000
CPU
15000
10000
5000
A
pr
-0
4
Ju
n04
A
ug
-0
4
O
ct
-0
4
D
ec
-0
4
Fe
b05
A
pr
-0
5
Ju
n05
A
ug
-0
5
O
ct
-0
5
D
ec
-0
5
Fe
b06
0
Date
INFSO-RI-508833
EGEE Resources
Enabling Grids for E-sciencE
#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
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
Input “sandbox”
Output “sandbox”
Resource
Broker
Job Query
Job Submit
Author.
&Authen.
Information
Service
Job Status
Storage
Element
Brokerinfo
Job Submission
Service
Logging &
Book-keeping
Output “sandbox”
Job Status
Compute
Element
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-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
Request
User client
GSI
VOMS1 signed information
VOMS2 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 DataBase
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 LCG2.3.0 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
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).
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
• Interactive
• MPICH
• Partitionable
• Checkpointable
a simple batch job
a job whose standard streams are
forwarded to the submitting client
a parallel application using MPICH-P4
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-sciencE
• 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
…
35000
30000
25000
Jobs / day
–
–
–
–
–
–
–
–
–
–
–
20000
15000
10000
5000
0
Jan05
Feb05
Mar- Apr-05 May05
05
Jun- Jul-05 Aug05
05
Sep- Oct-05 Nov05
05
Dec05
Jan06
Feb06
Mar- Apr-06
06
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
User Interface
SRM (DPM)
FTS
LFC
local 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”
Where grids can help addressing
neglected diseases
Enabling Grids for E-sciencE
• 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-sciencE
• 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-sciencE
Grid service customers
DC 1& DC2 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-sciencE
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 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
First initiative on in silico drug discovery
against emerging diseases
Enabling Grids for E-sciencE
• 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
H5
N1
•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
INFSO-RI-508833
FP6−2004−Infrastructures−6-SSA-026024
EUMEDGRID Geography
INCO: International Scientific Cooperation Projects
SUSTAINABLE WATER MANAGEMENT IN
MEDITERRANEAN COASTAL AQUIFERS:
Recharge Assessment and Modelling Issues
(SWIMED)
ICA3-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
Paramètres hydrodynamiques
Transmissivités de l’aquifère
Altitude du substratum
N
$
$
$
W
E
$
$
$
S
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$ $
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$
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0
T
$
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$
T
$
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$
$
S
$
$
$
T
$
E
W
$
$
$
N
$
$
$
$
$
Données
Altitude du substratum [m]
-403 - -363
-363 - -324
-324 - -284
-284 - -245
-245 - -205
-205 - -166
-166 - -126
-126 - -87
-87 - -47
-47 - -7
-7 - 32
32 - 72
72 - 111
111 - 151
151 - 190
No Data
T
$
T
$
T
$
T
$
$
T
T
$
T
T$
$
T
T$
$
T
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$
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$
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T T
$
T T
$$
$ $
T
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T
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TT
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$T $
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$
T
T
$
T
$
T
$
8 Kilometers
0
5
10 Kilometers
Aq-oued.shp
T
$
Points.shp
Transmissivité
0.00004 - 0.00087
0.00087 - 0.0017
0.0017 - 0.00252
0.00252 - 0.00335
0.00335 - 0.00418
0.00418 - 0.00501
0.00501 - 0.00583
0.00583 - 0.00666
0.00666 - 0.00749
0.00749 - 0.00831
0.00831 - 0.00914
0.00914 - 0.00997
No Data
Aquifer.shp
Manque remarquable de données de transmissivité !!
ArchaeoGRID (1/2)
Enabling Grids for E-sciencE
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)
ArchaeoGRID (2/2)
Enabling Grids for E-sciencE
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 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.