1. Distributed Services for Grid Distributed Services for Grid
Enabled Data Analysis
Distributed Services for Grid
Enabled Data Analysis

2. Scenario Liz and John are members of CMS
Liz is from Caltech and is an expert in event reconstruction
John is from Florida and is an expert in statistical fits
They wish to combine their expertise and collaborate on a CMS Data Analysis Project

3. Grid-services Web Service: Clarens
Demo Goals
Prototype vertically integrated system
Transparent/seamless experience
Distribute grid services using a uniform web service
Clarens !
Understand system
latencies
failure modes
Investigate request scheduling in a resource limited and dynamic environment
Emphasize functionality over scalability
Investigate interactive vs. scheduled data analysis on a grid
Hybrid example
Understand where are the difficult issues
Grid Monitoring
Service
MonALISA
Grid Resource
VDT Server
Grid Execution
VDT Client
Grid Scheduling
Sphinx
Virtual Data
Chimera
Workflow
Generation
ShahKar
Collaborative
Environment
CAVE
Grid-services Web Service: Clarens
Analysis Client
IGUANA
ROOT
Web Browser
PDA
Remote Data
Clarens

4. Data Discovery Chimera Virtual data products are
pre-registered with the Chimera Virtual
Data Service.
Using Clarens, data products are
discovered by Liz and John by remotely
browsing the Chimera Virtual Data Service
y.cards
x.cards
pythia
pythia
y.ntpl
x.ntpl
h2root
h2root
y.root
x.root
y.ntpl
y.root
x.ntpl
x.root
request
browse
Chimera
Virtual Data System

5. Data Analysis Chimera Liz wants to analyse x.root
using her analysis code a.C
x.cards
pythia
// Analysis code: a.C
#include <iostream.h>
#include <math.h>
#include "TFile.h"
#include "TTree.h"
#include "TBrowser.h"
#include "TH1.h"
#include "TH2.h"
#include "TH3.h"
#include "TRandom.h"
#include "TCanvas.h"
#include "TPolyLine3D.h"
#include "TPolyMarker3D.h"
#include "TString.h"
void a( char treefile[], char newtreefile[] ) {
Int_t Nhep;
Int_t Nevhep;
Int_t Isthep[3000];
Int_t Idhep[3000], Jmohep[3000][2], Jdahep[3000][2];
Float_t Phep[3000][5], Vhep[3000][4];
Int_t Irun, Ievt;
Float_t Weight;
Int_t Nparam;
Float_t Param[200];
TFile *file = new TFile( treefile );
TTree *tree = (TTree*) file -> Get( "h10
tree -> SetBranchAddress( "Nhep", &Nh
x.ntpl
h2root
x.root
Chimera
Virtual Data System

6. Interactive Workflow Generation
Liz browses the local directory
for her analysis code and the Chimera
Virtual Data Service for input LFNs…
x.cards
pythia
x.ntpl
Select input LFN
h2root
x.root
Select CINT script
Define output LFN
Chimera
Virtual Data System
register
browse

7. Interactive Workflow Generation
She selects and registers (to the
Grid) her analysis code, the
appropriate input LFN, and a newly
defined ouput LFN
x.cards
pythia
x.ntpl
Select input LFN
a.C
b.C
c.C
d.C
y.ntpl
y.root
x.ntpl
x.root
h2root
x.root
Select CINT script
Define output LFN
xa.root
Chimera
Virtual Data System
register
browse

8. Interactive Workflow Generation
A branch is automatically added
in the Chimera Virtual Data
Catalog, and a.C is uploaded into
“gridspace” and registered with RLS
x.cards
pythia
x.ntpl
Select input LFN
a.C
b.C
c.C
d.C
y.ntpl
y.root
x.ntpl
x.root
h2root
a.C
x.root
a.C
x.root
root
Select CINT script
Define output LFN
xa.root
xa.root
Chimera
Virtual Data System
register
browse

9. Interactive Workflow Generation
x.cards
Querying the Virtual Data Service,
Liz sees that xa.root is now available
to her as a new virtual data product
pythia
x.ntpl
y.ntpl
y.root
x.ntpl
x.root
xa.root
h2root
x.root
a.C
root
request
browse
xa.root
Chimera
Virtual Data System

10. Request Submission Chimera She requests it…. y.ntpl y.root x.ntpl
x.cards
She requests it….
pythia
x.ntpl
y.ntpl
y.root
x.ntpl
x.root
xa.root
h2root
x.root
a.C
xa.root
root
request
browse
xa.root
Chimera
Virtual Data System

11. Brief Interlude: The Grid is Busy and Resources are Limited!
Production is taking place
Other physicists are using the system
Use MonALISA to avoid congestion in the grid
Limited:
As grid computing becomes standard fare, oversubscription to resources will be common !
CMS gives Liz a global high priority
Based upon local and global policies, and current Grid weather, a grid-scheduler:
must schedule her requests for optimal resource use

12. Sphinx Scheduling Server
Nerve Centre
Global view of system
Data Warehouse
Information driven
Repository of current state of the grid
Control Process
Finite State Machine
Different modules modify jobs, graphs, workflows, etc and change their state
Flexible
Extensible
Sphinx Server
Message Interface
Graph Reducer
Control Process
Job Predictor
Data Warehouse
Graph Predictor
Job Admission Control
Policies
Accounting Info
Grid Weather
Resource Prop.
and status
Request Tracking
Workflows
etc
Graph Admission Control
Graph Data Planner
Job Execution Planner
Graph Tracker
Data Management
Information Gatherer

13. Distributed Services for Grid Distributed Services for Grid
Enabled Data Analysis
Distributed Services for Grid
Enabled Data Analysis
Caltech
File
Service
VDT Resource
Chimera
Virtual Data
Service
Clarens
ROOT
Data Analysis
Client
Florida
File
Service
VDT Resource
Clarens
Sphinx/VDT
Execution
Service
Globus
Clarens
GridFTP
Clarens
Fermilab
File
Service
VDT Resource
Sphinx
Scheduling
Service
Globus
Iowa
File
Service
VDT Resource
Globus
RLS
Replica
Location
Service
MonALISA
Monitoring
Service
MonALISA

14. Collaborative Analysis
x.cards
Meanwhile, John has been
developing his statistical fits in b.C
by analysing the data product x.root
pythia
x.ntpl
h2root
y.ntpl
y.root
x.ntpl
x.root
xa.root
xb.root
x.root
a.C
b.C
root
root
xb.root
xa.root
xb.root
request
browse

15. Collaborative Analysis
x.cards
After Liz has finished optimising
the event reconstruction, John uses his
analysis code b.C on her data product
xa.root to produce the final statistical fits
and results !
pythia
x.ntpl
h2root
y.root
x.ntpl
x.root
xa.root
xb.root
xab.root
x.root
a.C
b.C
root
root
xab.root
xa.root
xb.root
request
browse
root
xab.root

16. Key Features Distributed Services Prototype in Data Analysis
Remote Data Service
Replica Location Service
Virtual Data Service
Scheduling Service
Grid-Execution Service
Monitoring Service
Smart Replication Strategies for “Hot Data”
Virtual Data w.r.t. Location
Execution Priority Management on a Resource Limited Grid
Policy Based Scheduling & QoS
Virtual Data w.r.t. Existence
Collaborative Environment
Sharing of Datasets
Use of Provenance

17. Credits California Institute of Technology University of Florida
Julian Bunn, Iosif Legrand, Harvey Newman, Suresh Singh, Conrad Steenberg, Michael Thomas, Frank Van Lingen, Yang Xia
University of Florida
Paul Avery, Dimitri Bourilkov, Richard Cavanaugh, Laukik Chitnis, Jang-uk In, Mandar Kulkarni, Pradeep Padala, Craig Prescott, Sanjay Ranka
Fermi National Accelerator Laboratory
Anzar Afaq, Greg Graham