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					     HENP Grids and Networks
    Global Virtual Organizations

   Harvey B Newman, Professor of Physics
LHCNet PI, US CMS Collaboration Board Chair
        WAN In Lab Site Visit, Caltech
Meeting the Advanced Network Needs of Science
                March 5, 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 by 2002, ~100 Petabytes by 2007,
      to ~1 Exabyte by ~2012.
 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
                                 pp    s = 14 TeV Ldesign = 1034 cm-2 s-1
                               Heavy ions       (e.g. Pb-Pb at s ~ 1000 TeV)


                                       27 Km ring
                                       1232 dipoles B=8.3 T
                                       (NbTi at 1.9 K)        CMS and ATLAS:
                                                              pp, general purpose

First Beams :
April 2007      ALICE :
                heavy ions,
Physics Runs:   p-ions                                  LHCb :
July 2007                                               pp, B-physics
                 LHC Collaborations

         ATLAS                        CMS

The US provides about 20-25% of
the author list in both experiments


               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
                            ~PByte/sec                                Tier0/( Tier1)/( Tier2)   ~1:1:1

                                             Online System              ~100-1500
Experiment                                                              MBytes/sec
                                                                                CERN 700k SI95
                                                         Tier 0 +1                ~1 PB Disk;
                                                                                  Tape Robot
                     ~2.5-10 Gbps
 Tier 1                                                                                      FNAL: 200k
     IN2P3 Center                    RAL Center                       INFN Center           SI95; 600 TB

                                                                                                           2.5-10 Gbps
                                               Tier 2                                   Tier2    Tier2  Tier2
                                                                                 Tier2 Center Center Center Center
                                                                      Tier2 Center
                     ~2.5-10 Gbps
   Tier 3
                       Institute Institute   Institute    Institute
                                                                       Physicists work on analysis “channels”
Physics data cache                    0.1–10 Gbps
                                                                       Each institute has ~10 physicists working
                                                 Tier 4                on one or more channels
      Transatlantic Net WG (HN, L. Price)
         Bandwidth Requirements [*]
       2001 2002       2003     2004     2005     2006
 CMS     100    200      300      600       800      2500
ATLAS     50    100      300      600       800      2500
BaBar    300    600     1100 1600          2300      3000
CDF      100    300      400     2000      3000      6000
D0       400    1600 2400 3200             6400      8000
BTeV      20     40      100      200       300      500
DESY     100    180      210      240       270      300

CERN     155-   622     2500 5000 10000 20000
 BW      310
  [*] Installed BW. Maximum Link Occupancy 50% Assumed
History – One large Research Site

    Much of the Traffic:
 SLAC  IN2P3/RAL/INFN;         Current Traffic ~400 Mbps;
                                     ESNet Limitation
    via ESnet+France;      Projections: 0.5 to 24 Tbps by ~2012
          Progress: Max. Sustained TCP Thruput
              on Transatlantic and US Links

   8-9/01
          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;
    and the Internet2 E2E Initiative:
             US-CERN OC48 Deployment
 American                  Phase One                   European
 partners                                               partners
                        OC12 (Production)
            Cisco   OC48 (Develop and Test)     Cisco
            7609                                7609

   Caltech (DOE)                                  CERN - Geneva
   PoP in Chicago

                           Phase two

            Cisco   OC48 (Develop and Test)
American    7606                                7606           European
partners                                                       partners
            Cisco        OC12 (Production)      Cisco       CERN
            7609                                 7609
       Caltech (DOE)                          CERN - Geneva
       PoP in Chicago
                    OC-48 deployment (Cont’d)
                              Phase three (Late 2002)
              Alcatel 7770                                                            Alcatel 7770
               DataTAG                                                                 DataTAG
                (CERN)                                                                  (CERN)

                                Optical Mux/Dmux

                                                                   Optical Mux/Dmux
                                  Datag (CERN)

                                                                     Datag (CERN)
             Cisco 7606                                                                 Cisco 7606
              Caltech                                                                    DataTAG     European
                (DoE)                                                                    (CERN)      partners
American                                             2.5 Gbps
partners      Juniper M10                                                             Juniper M10
                Caltech                                                                DataTAG
                 (DoE)                                                                  (CERN)

              Cisco 7609                           OC12 622 Mbps                       Cisco 7609      CERN
               Caltech                                                                  DataTAG
                 (DoE)                                                                  (CERN)

    Separate environments for tests and production
    Transatlantic testbed dedicated to advanced optical network research
      and intensive data access applications
                  DataTAG Project

   GEANT                 GENEVA

                    SURFnet                     STAR-TAP                2
      Fr                          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
                LHCnet Network : March 2003
                GEANT              Switch            IN2P3                WHO

CERN -Geneva
                                            Alcatel 7770     Cisco 7609    Juniper M10
                      Linux PC for           DataTAG          DataTAG       DataTAG
                    Performance tests         (CERN)          (CERN)         (CERN)
   Cisco 7606
                      & Monitoring
     CERN                                                  Optical Mux/Dmux
                                                             Alcatel 1670

               OC12 – 622 Mbps                                       OC48 – 2,5 Gbps

                          Linux PC for                     Optical Mux/Dmux
    Cisco 7606                                               Alcatel 1670
                        Performance tests
                          & Monitoring
                                            Alcatel 7770     Cisco 7609   Juniper M10
                                             DataTAG          Caltech       Caltech
                                              (CERN)            (DoE)        (DoE)
Caltech/DoE PoP – StarLight Chicago

     Abilene             ESnet          NASA                MREN              STARTAP
Development and tests
 FAST (Caltech): A Scalable, “Fair” Protocol                                                        SC2002
 for Next-Generation Networks: from 0.1 To 100 Gbps                                                   11/02

                                                               Highlights of FAST TCP
                                                            Standard Packet Size
                                                            940 Mbps single flow/GE card
                                                                  9.4 petabit-m/sec
                                                                  1.9 times LSR
                                               SC2002       9.4 Gbps with 10 flows
                                               10 flows           37.0 petabit-m/sec
                                                                  6.9 times LSR
I2 LSR                                          2 flows     22 TB in 6 hours; in 10 flows
multiple                                        SC2002     Implementation
                                                1 flow      Sender-side (only) mods
1 flow                                                      Delay (RTT) based
22.8.02                                                     Stabilized Vegas

 Internet: distributed feedback system        Theory      Experiment

                              Rf (s)
            TCP                                 AQM        Sunnyval                                   Baltimore
                                                    p      e
                              Rb’(s)                                   3000km           1000km

URL:           Next: 10GbE; 1 GB/sec disk to disk                 C. Jin, D. Wei, S. Low
                                                                                         FAST Team & Partners
                       TeraGrid (
                     NCSA, ANL, SDSC, Caltech, PSC
A Preview of the Grid Hierarchy
 and Networks of the LHC Era



                                                   Starlight / NW Univ
             San Diego
                                     I-WIRE         Multiple Carrier Hubs

                                       ANL             Ill Inst of Tech

     OC-48 (2.5 Gb/s, Abilene)                     Univ of Chicago
     Multiple 10 GbE (Qwest)                                              Indianapolis
     Multiple 10 GbE                           NCSA/UIUC                  (Abilene NOC)
     (I-WIRE Dark Fiber)
                                         Source: Charlie Catlett, Argonne
                           National Light Rail


 SAC                                                                NYC     BOS
                 OGD                           CHI
SVL                          DEN                       CLE
               FRE                                           PIT          WDC
      LAX            PHO
                                                 WAL         ATL                Buildout Started
              SDG          OLG
                                                                                 November 2002
                                                                                Initially 4 10G
                                                                                To 40 10G
            15808 Terminal, Regen or OADM site                                   Waves in
            Fiber route                                                          Future
               Transition now to optical, multi-wavelength R&E networks:
             US, Europe and Intercontinental (US-China-Russia) Initiatives;
             Efficient use of Wavelengths is an Essential Part of this Picture
            HENP Major Links: Bandwidth
            Roadmap (Scenario) in Gbps
Year        Production     Experimental        Remarks
2001          0.155         0.622-2.5         SONET/SDH

2002          0.622            2.5           SONET/SDH
                                           DWDM; GigE Integ.
2003            2.5             10         DWDM; 1 + 10 GigE
2005            10           2-4 X 10             Switch;
                                               Provisioning
2007         2-4 X 10       ~10 X 10;        1st Gen.  Grids
                             40 Gbps
2009         ~10 X 10       ~5 X 40 or            40 Gbps 
           or 1-2 X 40     ~20-50 X 10
2011        ~5 X 40 or     ~25 X 40 or         2nd Gen  Grids
                                             Terabit Networks
             ~20 X 10       ~100 X 10
2013         ~Terabit       ~MultiTbps         ~Fill One Fiber
Continuing the Trend: ~1000 Times Bandwidth Growth Per Decade;
 We are Rapidly Learning to Use and Share Multi-Gbps Networks
                    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)
 Access Grid; VRVS: supporting
  group-based collaboration
 Genomics, Proteomics, ...
 The Earth System Grid and EOSDIS
 Federating Brain Data
 Computed MicroTomography   …
 Virtual Observatories
 COJAC: CMS ORCA Java Analysis Component:
   Java3D Objectivity JNI Web Services

    Demonstrated Caltech-Rio
de Janeiro (2/02) and Chile (5/02)
 CMS Analysis – an Integrated Grid Enabled Environment
    CIT, UCSD, Riverside, Davis; + UCLA, UCSB
  NSF ITR – 50% so far
  Lightweight, functional, making use
   of existing software AFAP
  Plug-in Architecture based on Web
  Expose Grid “Global System” to
   physicists – at various levels of
   detail with Feedback
  Supports Request, Preparation,
   Production, Movement, Analysis
   of Physics Object Collections
  Initial Target: Californian US-CMS
  Future: Whole US CMS and CMS

 The Clarens Remote (Light-
  Client) Dataserver: a WAN
  system for remote data
 Clarens servers are deployed
  at Caltech, Florida, UCSD,
  FNAL, Bucharest; Extend to
 SRB now installed as Clarens
  service on Caltech Tier2
  (Oracle backend)
                 NSF ITR: Globally Enabled
                  Analysis Communities
 Develop and build
  Dynamic Workspaces
 Build Private Grids to support
 scientific analysis communities
    Using Agent Based
     Peer-to-peer Web Services
 Construct Autonomous
  Communities Operating
  Within Global Collaborations
 Empower small groups of
  scientists (Teachers and
  Students) to profit from and
  contribute to int’l big science
 Drive the democratization of
  science via the deployment of
  new technologies
                 NSF ITR: Key New Concepts

   Dynamic Workspaces
     Provide capability for individual and
      sub-community to request and receive
      expanded, contracted or otherwise
      modified resources, while maintaining
      the integrity and policies of the Global
   Private Grids
     Provide capability for individual and
     community to request, control and use
     a heterogeneous mix of Enterprise wide
     and community specific software, data,
     meta-data, resources
 Build on Global Managed End-to-end Grid
  Services Architecture; Monitoring System
 Autonomous, Agent-Based, Peer-to-Peer
Private Grids and P2P Sub-
Communities in Global CMS
        A Global Grid Enabled Collaboratory for
             Scientific Research (GECSR)
     A joint ITR proposal from The first Grid-enabled
Caltech (HN PI,JB:CoPI)        Collaboratory: Tight
Michigan (CoPI,CoPI)           integration between
Maryland (CoPI)                 Science of
    and Senior Personnel from Globally scalable
                                   working environment
Lawrence Berkeley Lab
                                 A Sophisticated Set
                                   of Collaborative Tools
Fermilab                          (VRVS, VNC; Next-Gen)
Arlington (U. Texas)            Agent based
Iowa                              monitoring and
Florida State                     decision support
                                   system (MonALISA)
 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
                                                          Services is
                                                       highlighted in the
                                                       report of Atkins et
                                                           al. 2003
           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
14000+ Hosts;
8000+ Registered Users
in 64 Countries
56 (7 I2) Reflectors
Annual Growth 2 to 3X
            MonaLisa: A Globally
       Scalable Grid Monitoring System
      By I. Legrand (Caltech)
   Deployed on US CMS Grid
   Agent-based Dynamic
    information / resource
    discovery mechanism
   Talks w/Other Mon. Systems
   Implemented in
      Java/Jini; SNMP
      WDSL / SOAP with UDDI
   Part of a Global Grid
    Control RoomService
          Distributed System Services Architecture
                (DSSA): CIT/Romania/Pakistan
 Agents: Autonomous, Auto-                                Discovery
    discovering, self-organizing,                           Service
    collaborative                          Lookup
   “Station Servers” (static) host                             Lookup
    mobile “Dynamic Services”                                   Service
   Servers interconnect dynamically;
    form a robust fabric in which
    mobile agents travel, with a    Station
    payload of (analysis) tasks     Server
   Adaptable to Web services:
    OGSA; and many platforms
   Adaptable to Ubiquitous,                 Station                   Station
                                             Server                    Server
    mobile working environments
    Managing Global Systems of Increasing Scope and Complexity,
    In the Service of Science and Society, Requires A New Generation
    of Scalable, Autonomous, Artificially Intelligent Software Systems
          MONARC SONN: 3 Regional Centres
           Learning to Export Jobs (Day 9)
           <E> = 0.83               <E> = 0.73
                     1MB/s ; 150 ms RTT
            CERN                          CALTECH
           30 CPUs                        25 CPUs

                          20 CPUs    <E> = 0.66

                                                    Day = 9
By I. Legrand
          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
        of wavelengths in the 1 to 10 Gbps range now, and
        higher speeds (e.g. 40 to 80 Gbps) in the near future
    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
    We will Work in Concert with AMPATH, Internet2, Terena, APAN;
        DataTAG, the Grid projects and the Global Grid Forum
 A WAN in Lab facility, available to the Community, is a Key
    Element in achieving these revolutionary goals
Some Extra
Slides Follow
             Global Networks for HENP

 National and International Networks, with sufficient
  (rapidly increasing) capacity and capability, are
  essential for
   The daily conduct of collaborative work in both
    experiment and theory
   Detector development & construction on a global scale;
    Data analysis involving physicists from all world regions
   The formation of worldwide collaborations
   The conception, design and implementation of
    next generation facilities as “global networks”
 “Collaborations on this scale would never have
  been attempted, if they could not rely on excellent
The Large Hadron Collider (2007-)

         The Next-generation Particle Collider
            The largest superconductor
                installation in the world
         Bunch-bunch collisions at 40 MHz,
          Each generating ~20 interactions
            Only one in a trillion may lead
               to a major physics discovery
         Real-time data filtering:
          Petabytes per second to Gigabytes
          per second
         Accumulated data of many Petabytes/Year
                Education and Outreach

QuarkNet has 50 centers nationwide (60 planned)

Each center
 2-6 physicist
     mentors
 2-12
* Depending on
year of the program
and local variations
                 A transatlantic testbed

 Multi platforms
  Vendor independent
  Interoperability tests
  Performance tests
 Multiplexing of optical signals into a single OC- 48
  transatlantic optical channel
 Layer 2 services:
   Circuit-Cross-Connect (CCC)
   Layer 2 VPN
 IP services:
 Future: GMPLS
   GMPLS is an extension of MPLS
              Service Implementation example

 Abilene & ESnet                                                             GEANT
                                                                                        Host 2
    10 GbE                                                               2,5 Gb/s
 VLAN 600                           2,5 Gb/s                             GbE          CERN
Host 1                                                                  OSPF

VLAN 601               Optical                  Optical
                      multiplexer              multiplexer
                                                                               Host 1
Host 2
             Host 3                                            Host 3

    StarLight- Chicago                                       CERN - Geneva
         Logical view of previous implementation

 Abilene & ESnet                                                  GEANT

                          2,5 Gb/s POS

                        IP over POS

Host 1   VLAN 600   Ethernet over MPLS over POS   VLAN 600   Host 1

         VLAN 601    IP over MPLS over POS             VLAN 601       Host 2
Host 2

                      Ethernet over POS                  Host 3
    Host 3
               Using Web Services for Tag Data
 Use ~180,000 Tag objects derived
  from di-jet ORCA events

 Each Tag: run & event number, OID
  of ORCA event object, then
  E,Phi,Theta,ID for 5 most energetic
  particles, and E,Phi,Theta for 5
  most energetic jets

 These Tag events have been used
  in various performance tests and
  demonstrations, e.g. SC2000,
  SC2001, comparison of Objy vs
  RDBMS query speeds (GIOD), as
  source data for COJAC, etc.
          DB-Independent Access to Object
          Collections: Middleware Prototype
 First layer ODBC provides
  database vendor
  abstraction, allowing any
  relational (SQL) database to
  be plugged into the system.

 Next layer OTL provides an
  encapsulation of the results
  of a SQL query in a form
  natural to C++, namely STL
  (standard template library)
  map and vector objects.

 Higher levels map C++
  object collections to the
  client’s required format, and
  transport the results to the
                   Reverse Engineer and Ingest
                    the CMS JETMET nTuples
 From the nTuple description:
   Derived an ER diagram for the content
   An AOD for the JETMET analysis
 We then wrote tools to:
   Automatically generate a set of SQL
    CREATE TABLE commands to create
    the RDBMS tables
   Generate SQL INSERT and bulk load
    scripts that enable population of the
    RDBMS tables
 We imported the ntuples into
   SQLServer at Caltech
   Oracle 9i at Caltech
   Oracle 9i at CERN
   PostgreSQL at Florida
 In the future:
     Generate a “Tag” table of data
      that captures the most often
      used data columns in the nTuple
  GAE Demonstrations
iGrid2002 (Amsterdam)
                         Clarens (Continued)
 Servers
   Multi-process (based on Apache), using XML/RPC
   Similar, but using SOAP
   Lightweight (using select loop) single-process
   Functionality: file access (read/download/part/whole), directory listing,
     file selection, file checksumming, access to SRB, security with PKI/VO
     infrastructure, RDBMS data selection/analysis, MonaLisa integration

 Clients
   ROOT client … browsing remote file repositories, files
   Platform-independent Python-based client for rapid prototyping
   Browser-based Java/Javascript client (in planning) … for use when no
    other client packages is desired
   Some clients of historical interest e.g. Objectivity

 Source/Discussion

 Future
   Expose POOL as remote data source in Clarens
   Clarens peer-to-peer discovery and communication
        Globally Scalable Monitoring Service
                                                                    I. Legrand
                    Lookup            Discovery
    Lookup                    Proxy                    Client
    Service                                       (other service)

 Push & Pull
  rsh & ssh        RC
scripts; snmp    Monitor   Component
     Farm        Service    Factory
                           GUI marshaling
                           Code Transport
                           RMI data access

 NSF/ITR: A Global Grid-Enabled
Collaboratory for Scientific Research
     Grid Analysis Environment

           CHEP 2001, Beijing
               Harvey B Newman
       California Institute of Technology
               September 6, 2001
                       GECSR Features

 Persistent Collaboration: desktop, small and large conference
   rooms, halls, Virtual Control Rooms
 Hierarchical, Persistent, ad-hoc peer groups (using Virtual
   Organization management tools)
 “Language of Access”: an ontology and terminology for users
  to control the GECSR.
  Example: cost of interrupting an expert,
  virtual open, and partly-open doors.
 Support for Human- System (Agent)-Human as well as Human-
  Human interactions
 Evaluation, Evolution and Optimisation of the GECSR
 Agent-based decision support for users

 The GECSR will be delivered in “packages” over the course of
                 GECSR: First Year Package
 NEESGrid: unifying interface and tool launch system
   CHEF Framework: portlets for file transfer using GridFTP,
     teamlets, announcements, chat,shared calendar, role-based
     access,threaded discussions,document repository
 GIS-GIB: a geographic-information-systems-based Grid
  information broker
 VO Management tools (developed for PPDG)
 Videoconferencing and shared desktop: VRVS and VNC
  ( and
 MonaLisa: real time system monitoring and user control

 The above tools already exist: the effort is in integrating and
  identifying the missing functionality.
           GECSR: Second Year Package
Enhancements, including:
Detachable windows
Web Services Definition (WSDL) for CHEF
Federated Collaborative Servers
Search capabilities
Learning Management Components
Grid Computational Portal Toolkit
Knowledge Book, HEPBook
Software Agents for intelligent searching etc.
Flexible Authentication
         Beyond Traditional Architectures:
                  Mobile Agents
       “Agents are objects with rules and legs” -- D. Taylor


  Mobile Agents: (Semi)-Autonomous,
Goal Driven, Adaptive
  Execute Asynchronously
  Reduce Network Load: Local Conversations
  Overcome Network Latency; Some Outages
  Adaptive  Robust, Fault Tolerant
  Naturally Heterogeneous
  Extensible Concept: Coordinated Agent

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