HENP Grids and Networks
Global Virtual Organizations
Harvey B Newman, Professor of Physics
LHCNet PI, US CMS Collaboration Board Chair
Drivers of the Formation of the Information Society
April 18, 2003
Computing Challenges:
Petabyes, Petaflops, Global VOs
 Geographical dispersion: of people and resources
 Complexity: the detector and the LHC environment
 Scale:
Tens of Petabytes per year of data
5000+ Physicists
250+ Institutes
60+ Countries
Major challenges associated with:
Communication and collaboration at a distance
Managing globally distributed computing & data resources
Cooperative software development and physics analysis
New Forms of Distributed Systems: Data Grids
Next Generation Networks for
Experiments: Goals and Needs
Large data samples explored and analyzed by thousands of
globally dispersed scientists, in hundreds of teams
 Providing rapid access to event samples, subsets
and analyzed physics results from massive data stores
 From Petabytes in 2003, ~100 Petabytes by 2008,
to ~1 Exabyte by ~2013.
 Providing analyzed results with rapid turnaround, by
coordinating and managing the large but LIMITED computing,
data handling and NETWORK resources effectively
 Enabling rapid access to the data and the collaboration
 Across an ensemble of networks of varying capability
 Advanced integrated applications, such as Data Grids,
rely on seamless operation of our LANs and WANs
 With reliable, monitored, quantifiable high performance
LHC Collaborations
US
ATLAS
The US provides about 20-25% of
the author list in both experiments
CMS
US LHC INSTITUTIONS
US CMS
Accelerator
US ATLAS
Four LHC Experiments: The
Petabyte to Exabyte Challenge
ATLAS, CMS, ALICE, LHCB
Higgs + New particles; Quark-Gluon Plasma; CP Violation
Data stored
~40 Petabytes/Year and UP;
CPU
0.30 Petaflops and UP
0.1 to
1
Exabyte (1 EB = 1018 Bytes)
(2008)
(~2013 ?) for the LHC Experiments
LHC: Higgs Decay into 4 muons
(Tracker only); 1000X LEP Data Rate
(+30 minimum bias events)
All charged tracks with pt > 2 GeV
Reconstructed tracks with pt > 25 GeV
109 events/sec, selectivity: 1 in 1013 (1 person in a thousand world populations)
LHC Data Grid Hierarchy
CERN/Outside Resource Ratio ~1:2
Tier0/( Tier1)/( Tier2)
~1:1:1
~PByte/sec
Online System
Experiment
~100-1500
MBytes/sec
Tier 0 +1
~2.5-10 Gbps
Tier 1
IN2P3 Center
INFN Center
RAL Center
Tier 2
CERN 700k SI95
~1 PB Disk;
Tape Robot
FNAL: 200k SI95;
600 TB
2.5-10 Gbps
Tier2 Center
Tier2 Center
Tier2 Center
Tier2 CenterTier2 Center
~2.5-10 Gbps
Tier 3
Institute Institute
~0.25TIPS
Physics data cache
Workstations
Institute
Institute
0.1–10 Gbps
Tier 4
Physicists work on analysis “channels”
Each institute has ~10 physicists working
on one or more channels
Transatlantic Net WG (HN, L. Price)
Bandwidth Requirements [*]
2001 2002

2003
2004
2005
2006
CMS
ATLAS
100
200
300
600
800
2500
50
100
300
600
800
2500
BaBar
300
600
2300
3000
CDF
D0
100
300
2000
3000
6000
1600 2400 3200
6400
8000
BTeV
DESY
20
40
100
200
300
500
100
180
210
240
270
300
CERN
BW
155310
622
400
1100 1600
400
2500 5000 10000 20000
[*] Installed BW. Maximum Link Occupancy 50% Assumed
See http://gate.hep.anl.gov/lprice/TAN
History – One large Research Site
Much of the Traffic:
SLAC  IN2P3/RAL/INFN;
via ESnet+France;
Abilene+CERN
Current Traffic ~400 Mbps;
ESNet Limitation
Projections: 0.5 to 24 Tbps by ~2012
Progress: Max. Sustained TCP Thruput
on Transatlantic and US Links




8-9/01
11/5/01
1/09/02
3/11/02
*
105 Mbps 30 Streams: SLAC-IN2P3; 102 Mbps 1 Stream CIT-CERN
125 Mbps in One Stream (modified kernel): CIT-CERN
190 Mbps for One stream shared on 2 155 Mbps links
120 Mbps Disk-to-Disk with One Stream on 155 Mbps
link (Chicago-CERN)
 5/20/02 450-600 Mbps SLAC-Manchester on OC12 with ~100 Streams
 6/1/02
290 Mbps Chicago-CERN One Stream on OC12 (mod. Kernel)
 9/02
850, 1350, 1900 Mbps Chicago-CERN 1,2,3 GbE Streams, OC48 Link
 11-12/02 FAST:
940 Mbps in 1 Stream SNV-CERN;
9.4 Gbps in 10 Flows SNV-Chicago
Also see http://www-iepm.slac.stanford.edu/monitoring/bulk/;
and the Internet2 E2E Initiative: http://www.internet2.edu/e2e
DataTAG Project
NewYork
ABILENE
UK
SuperJANET4
It
GARR-B
STARLIGHT
ESNET
GENEVA
GEANT
NL
SURFnet
Fr
INRIA
Atrium
STAR-TAP
CALREN
2
VTHD
 EU-Solicited Project. CERN, PPARC (UK), Amsterdam (NL), and INFN (IT);
and US (DOE/NSF: UIC, NWU and Caltech) partners
 Main Aims:
 Ensure maximum interoperability between US and EU Grid Projects
 Transatlantic Testbed for advanced network research
 2.5 Gbps Wavelength Triangle from 7/02; to 10 Gbps Triangle by Early 2003
FAST (Caltech): A Scalable, “Fair” Protocol
for Next-Generation Networks: from 0.1 To 100 Gbps
SC2002
11/02
Highlights of FAST TCP
SC2002
10 flows
SC2002
2 flows
I2 LSR
29.3.00
multiple
SC2002
1 flow
9.4.02
1 flow
 Standard Packet Size
 940 Mbps single flow/GE card
9.4 petabit-m/sec
1.9 times LSR
 9.4 Gbps with 10 flows
37.0 petabit-m/sec
6.9 times LSR
 22 TB in 6 hours; in 10 flows
Implementation
 Sender-side (only) mods
22.8.02
IPv6
Internet: distributed feedback system
Theory
Experiment
AQM
Sunnyval
e
7000km
 Delay (RTT) based
 Stabilized Vegas
Rf (s)
TCP
Rb’(s)
URL: netlab.caltech.edu/FAST
p
3000km
Next: 10GbE; 1 GB/sec disk to disk
Geneva
Baltimore
1000km
Chicago
C. Jin, D. Wei, S. Low
FAST Team & Partners
FAST TCP: Baltimore/Sunnyvale




RTT estimation: fine-grain timer
Fast convergence to equilibrium
Delay monitoring in equilibrium
Pacing: reducing burstiness
Measurements
 Std Packet Size
 Utilization averaged
over > 1hr
 3000 km Path
9G
90%
10G
88%
90%
Average
utilization
92%
95%
1 flow
2 flows
7 flows
Fair Sharing
Fast Recovery
8.6 Gbps;
21.6 TB
in 6 Hours
9 flows
10 flows
10GigE Data Transfer Trial; Internet2 LSR 2003
On Feb. 27-28, a Terabyte of data was transferred in 3700
seconds by S. Ravot of Caltech between the Level3 PoP in
Sunnyvale near SLAC and CERN through the TeraGrid
router at StarLight from memory to memory
As a single TCP/IP stream at average rate of 2.38 Gbps.
(Using large windows and 9kB “Jumbo frames”)
This beat the former record by a factor of ~2.5, and
European Commission
used the US-CERN link at 99% efficiency.
10GigE NIC
TeraGrid (www.teragrid.org)
NCSA, ANL, SDSC, Caltech, PSC
Abilene
Chicago
Indianapolis
Urbana
Caltech
San Diego
UIC
I-WIRE
ANL
A Preview
of the
GridAbilene)
Hierarchy
OC-48 (2.5
Gb/s,
and
Networks
of the
LHC Era
Multiple
10 GbE
(Qwest)
Higgs Study
at Caltech
Multiple
10 GbEand Production
with FAST
TCPDark
is a Flagship
(I-WIRE
Fiber) TeraGrid
Appication
Starlight / NW Univ
Multiple Carrier Hubs
Ill Inst of Tech
Univ of Chicago
NCSA/UIUC
Indianapolis
(Abilene NOC)
Source: Charlie Catlett, Argonne
National Light Rail
Footprint
SEA
POR
SAC
NYC
CHI
OGD
DEN
SVL
CLE
FRE
PIT
BOS
WDC
KAN
NAS
STR
LAX
RAL
PHO
SDG
WAL
OLG
ATL
DAL
JAC
15808 Terminal, Regen or OADM site
Fiber route
NLR
Buildout Started
November 2002
Initially 4 10G
Wavelengths
To 40 10G
Waves in
Future
Transition now to optical, multi-wavelength R&E networks:
US, Europe and Intercontinental (US-China-Russia) Initiatives;
HEP is the universally recognized leading application for initial NLR use
HENP Major Links: Bandwidth
Roadmap (Scenario) in Gbps
Year
Production
Experimental
2001
2002
0.155
0.622
0.622-2.5
2.5
2003
2.5
10
DWDM; 1 + 10 GigE
Integration
2005
10
2-4 X 10
 Switch;
 Provisioning
2007
2-4 X 10
1st Gen.  Grids
2009
~10 X 10
or 1-2 X 40
~5 X 40 or
~20 X 10
~T erabit
~10 X 10;
40 Gbps
~5 X 40 or
~20-50 X 10
~25 X 40 or
~100 X 10
2011
2013
~MultiTbps
Remarks
SONET/SDH
SONET/SDH
DWDM; GigE Integ.
40 Gbps 
Switching
2nd Gen  Grids
Terabit Networks
~Fill One Fiber
Continuing the Trend: ~1000 Times Bandwidth Growth Per Decade;
We are Learning to Use and Share Multi-Gbps Networks Efficiently;
HENP is leading the way towards future networks & dynamic Grids
HENP Lambda Grids:
Fibers for Physics
 Problem: Extract “Small” Data Subsets of 1 to 100 Terabytes







from 1 to 1000 Petabyte Data Stores
Survivability of the HENP Global Grid System, with
hundreds of such transactions per day (circa 2007)
requires that each transaction be completed in a
relatively short time.
Example: Take 800 secs to complete the transaction. Then
Transaction Size (TB)
Net Throughput (Gbps)
1
10
10
100
100
1000 (Capacity of
Fiber Today)
Summary: Providing Switching of 10 Gbps wavelengths
within ~3-5 years; and Terabit Switching within 5-8 years
would enable “Petascale Grids with Terabyte transactions”,
as required to fully realize the discovery potential of major HENP
programs, as well as other data-intensive fields.
Emerging Data Grid
User Communities
 NSF Network for Earthquake Engineering
Simulation (NEES)
Integrated instrumentation,
collaboration, simulation
 Grid Physics Network (GriPhyN)
ATLAS, CMS, LIGO, SDSS
 Access Grid; VRVS: supporting
group-based collaboration
And
 Genomics, Proteomics, ...
 The Earth System Grid and EOSDIS
 Federating Brain Data
 Computed MicroTomography
 Virtual Observatories
…
Particle Physics Data Grid
Collaboratory Pilot (2001-2003)
“The Particle Physics Data Grid Collaboratory Pilot will develop, evaluate and
deliver vitally needed Grid-enabled tools for data-intensive collaboration in
particle and nuclear physics. Novel mechanisms and policies will be
integrated with Grid Middleware, experiment specific applications and
computing resources to provide effective end-to-end capability.”
A Strong Computer Science/Physics
Partnership
Reflected in a groundbreaking
DOE MICS/HENP Partnership
Driving Forces: Now-running & future
experiments, ongoing CS projects;
leading-edge Grid develoments
Focus on End-to-end services
Integration of Grid Middleware with
Experiment-specific Components
Security: Authenticate, -orize, Allocate
Practical Orientation: Monitoring,
instrumentation, networks
PPDG Mission and Focii Today
 Mission: Enabling new scales of research in experimental
physics and experimental computer science
 Advancing Grid Technologies by addressing key issues
in architecture, integration, deployment and robustness
 Vertical Integration of Grid Technologies into the
Applications frameworks of Major Experimental Programs
 Ongoing as Grids, Networks and Applications Progress
 Deployment, hardening and extensions of Common
Grid services and standards
 Data replication, storage and job management,
monitoring and task execution-planning.
 Mission-oriented, Interdisciplinary teams of physicists,
software and network engineers, and computer scientists
 Driven by demanding end-to-end applications of
experimental physics
PPDG Accomplishments (I)

First-Generation HENP Application Grids; Grid Subsystems
 Production Simulation Grids for ATLAS and CMS;
STAR Distributed Analysis Jobs: to 30+ TBytes, 30+ Sites
 Data Replication for BaBar: Terabyte Stores systematically
replicated from California to France and the UK.
 Replication and Storage Management for STAR and JLAB:
Development and Deployment of Standard APIs, and
Interoperable Implementations.
 Data Transfer, Job and Information Management for D0:
GridFTP integrated with SAM; Condor-G job scheduler,
MDS resource discovery all integrated with SAM.

Initial Security Infrastructure for Virtual Organizations:
 PKI certificate management, policies and trust relationships
(using DOE Science Grid and Globus)
 Standardizing Authorization mechanisms: standard callouts for Local
Center Authorization for Globus, EDG
 Prototyping secure credential stores
 Engagement of site security teams
PPDG Accomplishments (II)
 Data and Storage Management:
 Robust data transfer over heterogeneous networks using standard
and next-generation protocols: GridFTP, bbcp; GridDT, FAST TCP
 Distributed Data Replica management: SRB, SAM, SRM
 Common Storage Management interface and services across diverse
implementations: SRM - HPSS, Jasmine, Enstore
 Object Collection management in diverse RDBMS’s: CAIGEE, SOCATS
 Job Planning, Execution and Monitoring:
 Job scheduling based on resource discovery and status:
Condor-G and extensions for strategy & policy
 Retry and Fault Tolerance in response to error conditions:
hardened gram, gass-cache, ftsh; Condor-G
 Distributed monitoring infrastructure for system tracking, resource
discovery, resource & job information: Monalisa, MDS, Hawkeye
 Prototypes and Evaluations
 Grid enabled physics analysis tools; prototypical environments
 End to end troubleshooting and fault handling
 Cooperative Monitoring of Grid, Fabric, Applications
WorldGrid: EU-US Interoperation
One of 24 Demos at SC2002
Collaborating with iVDGL and DataTAG
on international grid testbeds will lead to
easier deployment of experiment grids
across the globe.
PPDG Collaborators Participated
in 24 SC2002 Demos
Progress in Interfacing Analysis
CAIGEE
Tools to the
Grid: CAIGEE
 CMS Analysis – an Integrated Grid Enabled Environment
 Lightweight, functional, making use






of existing software AFAP
Plug-in Architecture based on Web
Services
Expose Grid Views of the
“Global System” to physicists –
at various levels of detail,
with Feedback
Supports Data Requests, Preparation,
Production, Movement, Analysis
of Physics Object Collections
Initial Target: US-CMS physicists in
California (CIT, UCSD, Riverside,
Davis, UCLA)
Expand to Include FIU, UF, FSU, UERJ
Future: Whole US CMS and CMS
CAIGEE Draft Architecture
ROOT
Laptop
Desktop
DAGs
Browser
PDA
Peer Group
Clarens
Super
Peer Group
Multiplatform,
Light
Client
Object Collection Access;
Interface to (O)RDBMS’s
Use of Web Services
File Transfer
Others
From GriPhyn, iVDGL,
Globus etc
Caltech/CMS
Developments
CMS Apps
MonaLisa
CAIGEE
ARCHITECTURE
PPDG: Common Technologies, Tools
& Applications: Who Uses What ?
Atlas
BaBar
CMS
D0
STAR TJNAF TJNAFLQCD
Globus
GridFTP
Globus MDS
Globus GSI
x
x
x
bbcp
x
x
x
x
x
(+KCA)
x/VDT
x
x
(+KCA)
x
Condorx/VDT
x/EDG
G/GRAM
SRM
x
x
SRB
x
x
Distributed
MAGDA Bbserver++
MOP
SAM
Data
Management
Analysis
JAS
Caigee root/sam
prototypes
Clarens
Automated
x
x
x
Data
Replication
Applications
p
p/EDG
x
x/sam
Grid
DOE SG
x
x
x
x
Certs
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Java
PPDG: Inter-Project
Collaborations and Interactions
US Physics Grid Projects
GriPhyN, iVDGL, PPDG – the Trillium
EU-US Physics Grids
LHC Computing Grid – ATLAS, CMS
European Data Grid - BaBar, D0, ATLAS, CMS
HEP Intergrid Coordination Board
Virtual Data Toolkit
SciDAC projects
Earth System Grid II
DOE Science Grid
A High Performance DataGrid Toolkit
Storage Resource Mngmnt for DG Apps
Security and Policy for Group Collaborations
Scientific Data Management Center
Bandwidth Estimation: Measurement
Methologies and Applications
A Nat’l Computational Infrastructure for
Lattice Gauge Theory
Distributed Monitoring Framework
Interaction
SRM
CA, RA
GlobusToolkit
SRM
GSI, CAS
STAR SDM
IEPEM-BW
TJNAF/LQCD
Netlogger, Glue Schema
PPDG CP + GriPhyN: Virtual Data Grids
“User’s View” of a PVDG: (PPDG-CP Proposal: April 2000)
Production Team
Individual
Investigator
Workgroups
Interactive User Tools
Virtual Data
Tools
Request Planning and
Scheduling Tools
Resource
Resource
Management
Management
Services
Services
Request Execution
Management Tools
Security
and
Security and
Policy
Policy
Services
Services
Other
Grid
Other Grid
Services
Services
Transforms
Data Source
Distributed resources
HENP Data Grids Versus
Classical Grids
 The original Computational and Data Grid concepts are
largely stateless, open systems: known to be scalable
 Analogous to the Web
 The classical Grid architecture has a number of implicit
assumptions
 The ability to locate and schedule suitable resources,
within a tolerably short time (i.e. resource richness)
 Short transactions with relatively simple failure modes
 HEP Grids are Data Intensive, and Resource-Constrained
 1000s of users competing for resources at 100s of sites
 Resource usage governed by local and global policies
 Long transactions; some long queues
 Need Realtime Monitoring and Tracking
 Distributed failure modes Strategic task management
Layered Grid Architecture
“Coordinating multiple resources”:
ubiquitous infrastructure services,
app-specific distributed services
Collective
Application
“Sharing single resources”: negotiating access, controlling use
Resource
“Talking to things”: communicat’n
(Internet protocols) & security
Connectivity
Transport
Internet
“Controlling things locally”:
Access to, & control of, resources
Fabric
Link
“The Anatomy of the Grid: Enabling Scalable Virtual Organizations”,
Foster, Kesselman, Tuecke, Intl J. High Performance Computing
Applications, 15(3), 2001.
Internet Protocol Architecture
Application
Current Grid Challenges: Secure
Workflow Management and Optimization
 Maintaining a Global View of Resources and System State
 Coherent end-to-end System Monitoring
 Adaptive Learning: new algorithms and strategies
for execution optimization (increasingly automated)
 Workflow: Strategic Balance of Policy Versus
Moment-to-moment Capability to Complete Tasks
 Balance High Levels of Usage of Limited Resources
Against Better Turnaround Times for Priority Jobs
 Goal-Oriented Algorithms; Steering Requests
According to (Yet to be Developed) Metrics
 Handling User-Grid Interactions: Guidelines; Agents
 Building Higher Level Services, and an Integrated
Scalable User Environment for the Above
HENP Grid Architecture:
Layers Above the Collective Layer

Physicists’ Application Codes
 Reconstruction, Calibration, Analysis

Experiments’ Software Framework Layer
 Modular and Grid-aware: Architecture able to interact
effectively with the lower layers (above)

Grid Applications Layer
(Parameters and algorithms that govern system operations)
 Policy and priority metrics
 Workflow evaluation metrics
 Task-Site Coupling proximity metrics

Global End-to-End System Services Layer
 Workflow monitoring and evaluation mechanisms
 Error recovery and long-term redirection mechanisms
 System self-monitoring, steering, evaluation and
optimisation mechanisms
 Monitoring and Tracking Component performance
Distributed System Services Architecture
(DSSA): CIT/Romania/Pakistan
 Agents: Autonomous, Auto-




discovering, self-organizing,
collaborative, adaptive
Lookup
“Station Servers” (static) host
Service
mobile “Dynamic Services”
Servers interconnect dynamically;
form a robust fabric in which
mobile agents travel, with a
Station
payload of (analysis) tasks
Server
Adaptable to Web services:
JINI/Javaspaces; WSDL/UDDI;
OGSA Integration/Migration planned Station
Adaptable to Ubiquitous Working
Server
Environments
Lookup
Discovery
Service
Lookup
Service
Managing Global Data Intensive Systems Requires A New
Generation of Scalable, Intelligent Software Systems
Station
Server
MonaLisa: A Globally
Scalable Grid Monitoring System
 Deployed on US CMS Test




Grid+CERN, Bucharest,
Taiwan, Pakistan
Agent-based Dynamic
information / resource
discovery mechanism
Talks w/Other Monitoring
Systems: MDS, Hawkeye
Implemented in
 Java/Jini; SNMP
 WDSL / SOAP with UDDI
For a Global “Grid
Monitoring” Service
MONARC SONN: 3 Regional Centres “Learning”
to Export Jobs (Day 9)
<E> = 0.83
CERN
30 CPUs
<E> = 0.73
1MB/s ; 150 ms RTT
Simulations for Strategy
Development
Self-Learning Algorithms
for Optimization are
Key Elements of Globally
Scalable Grids
NUST
20 CPUs
CALTECH
25 CPUs
<E> = 0.66
Optimized:
Day = 9
Increased functionality,
standardization
Grids and Open Standards:
the Move to OGSA
App-specific
Services
Web services
X.509,
LDAP,
FTP, …
Custom
solutions
Open Grid
Services Arch
GGF: OGSI, …
(+ OASIS, W3C)
Globus Toolkit Multiple implementations,
including Globus Toolkit
Defacto standards
GGF: GridFTP, GSI
Time
OGSA Example:
Reliable File Transfer Service
 A standard substrate:
the Grid service
 Standard interfaces &
behaviors; to address
key distributed system
Client
Client
Client
issues
 Refactoring, extension
of Globus Toolkit
Request & manage file transfer operations
protocol suite
Fault
Monito
r
Perf.
Monitor
File
Notf’n Policy
Grid
Service Transfer Source
Pending
interfaces
Faults
service Internal
data
elements State
Query &/or
Performnce
subscribe
Policy
to service data
Data transfer operations
2003 ITR Proposals: Globally Enabled
Analysis Communities
 Develop and build
Dynamic Workspaces
 Construct Autonomous
Communities Operating
Within Global Collaborations
 Build Private Grids to support
scientific analysis communities
 e.g. Using Agent Based
Peer-to-peer Web Services
 Drive the democratization of
science via the deployment of
new technologies
 Empower small groups of
scientists (Teachers and
Students) to profit from and
contribute to int’l big science
Private Grids and P2P SubCommunities in Global CMS
GECSR
 Initial targets are the global HENP collaborations, but GESCR is
expected to be widely applicable to other large scale collaborative
scientific endeavors
 “Giving scientists from all world regions the means to function
as full partners in the process of search and discovery”
The importance of
Collaboration
Services is
highlighted in the
Cyberinfrastructure
report of Atkins et
al. 2003
A Global Grid Enabled Collaboratory for
Scientific Research (GECSR)

A joint ITR proposal from The first Grid-enabled
Collaboratory: Tight
Caltech (HN PI,JB:CoPI)
integration between
Michigan (CoPI,CoPI)
Science of
Maryland (CoPI)
Collaboratories,

and Senior Personnel from Globally scalable
working environment
Lawrence Berkeley Lab
A Sophisticated Set
Oklahoma
of Collaborative Tools
Fermilab
(VRVS, VNC; Next-Gen)
Arlington (U. Texas)
Agent based
Iowa
monitoring and
Florida State
decision support
system (MonALISA)
14000+ Hosts;
8000+ Registered Users
in 64 Countries
56 (7 I2) Reflectors
Annual Growth 2 to 3X
Networks, Grids, HENP and WAN-in-Lab
Current generation of 2.5-10 Gbps network backbones arrived
in the last 15 Months in the US, Europe and Japan
 Major transoceanic links also at 2.5 - 10 Gbps in 2003
 Capability Increased ~4 Times, i.e. 2-3 Times Moore’s
 Reliable high End-to-end Performance of network applications
(large file transfers; Grids) is required. Achieving this requires:
 A Deep understanding of Protocol Issues, for efficient use
 Getting high performance (TCP) toolkits in users’ hands
 End-to-end monitoring; a coherent approach
 Removing Regional, Last Mile Bottlenecks and Compromises
in Network Quality are now On the critical path, in all regions
HENP is working in Concert with Internet2, TERENA,

AMPATH APAN; DataTAG, the Grid projects and the Global
Grid Forum to solve these problems
ICFA Standing Committee on
Interregional Connectivity (SCIC)
 Created by ICFA in July 1998 in Vancouver ; Following ICFA-NTF
 CHARGE:

Make recommendations to ICFA concerning the connectivity
between the Americas, Asia and Europe (and network requirements of
HENP)
As part of the process of developing these
recommendations, the committee should
 Monitor traffic
 Keep track of technology developments
 Periodically review forecasts of future
bandwidth needs, and
 Provide early warning of potential problems
 Create subcommittees when necessary to meet the charge
 Representatives: Major labs, ECFA, ACFA, NA Users, S. America
 The chair of the committee should report to ICFA once per
year, at its joint meeting with laboratory directors (Feb. 2003)
SCIC in 2002-3
A Period of Intense Activity
Formed WGs in March 2002; 9 Meetings in 12 Months
Strong Focus on the Digital Divide
Presentations at Meetings and Workshops
(e.g. LISHEP, APAN, AMPATH, ICTP and ICFA Seminars)
HENP more visible to governments: in the WSIS Process
Five Reports; Presented to ICFA Feb. 13,2003
See http://cern.ch/icfa-scic
Main Report: “Networking for HENP” [H. Newman et al.]
Monitoring WG Report
[L. Cottrell]
Advanced Technologies WG Report [R. Hughes-Jones,
O. Martin et al.]
Digital Divide Report
[A. Santoro et al.]
Digital Divide in Russia Report
[V. Ilyin]
SCIC Work in 2003
 Continue Digital Divide Focus
 Improve and Systematize Information in Europe;
in Cooperation with TERENA and SERENATE
 More in-depth information on Asia, with APAN
 More in-depth information on South America, with AMPATH
 Begin Work on Africa, with ICTP
 Set Up HENP Networks Web Site and Database
Share Information on Problems, Pricing; Example Solutions
 Continue and if Possible Strengthen Monitoring Work (IEPM)
 Continue Work on Specific Improvements:
 Brazil and So. America; Romania; Russia; India;
Pakistan, China
 An ICFA-Sponsored Statement at the World Summit on the
Information Society (12/03 in Geneva), prepared by SCIC +CERN
 Watch Requirements; the “Lambda” & “Grid Analysis” revolutions
 Discuss, Begin to Create a New “Culture of Collaboration”