1 Virtual EZ Grid AAA/Switch project Nabil Abdennadher HES-SO, hepia (Geneva)

1

Virtual EZ GridAAA/Switch project

Nabil Abdennadher

HES-SO, hepia (Geneva)

2Swiss Grid Day, Bern, 26th Nov. 2009

Virtual EZ Grid at a glance

• AAA/Switch funding

• Partners: UniGE, USI, UniNE, HES-SO

• Start/End dates : February 2009 - July 2010

• Objectives :

• Build a sustainable desktop Grid platform based on a Volunteer Computing (VC) middleware

• Evaluate it in a real world setting with two medical applications


Virtual EZ Grid objectives• Infrastructure: build a grid platform with more than 1000 PCs

• Non dedicated nodes

• Belong to several institutions.

• Reliability: manage the volatility of the “worker” nodes

• Check pointing, migration, restarting, etc.

• Security: guarantee the security of resources providers

• Negotiation Model: provide a resource-credit system

• Applications: two medical packages

• NeuroWeb: build neural maps extended from brain measurements

• MedGift: medical image analysis application


PLAN

• Virtual EZ Grid ingredients

• JOpera

• XtremWeb-CH

• EZ Grid

• Applications


Virtual EZ Grid ingredients

JOpera (developed at USI)

•A grid workflow management system: http://www.jopera.org

XtremWeb-CH (developed at HES-SO)

•A volunteer computing platform : www.xtremwebch.net

EZ grid (developed at UniNE and UniGE)

•Based on virtualization

•Job check-pointing

•Migration and restarting

ARC


Virtual EZ Grid architecture

Infrastructure

EZ-Grid

XtremWeb-CH (XWCH)

JOpera

ARC


JOpera

“Provide the scientist with a platform that takes care of all data handling and record keeping chores so that the user can concentrate on the science and not computer science”

Make it easy to build Grid and VC applications composed of multiple jobs

Based on slides by Cesare Pautasso


Drag, Drop and Connect



Run, Monitor, Steer and Debug



XtremWeb-CHcoordinator

Service Request

result

PC

Work Request

Work Result

Consumer(Users’ applications)

Providers(Workers)

PC

PC

PC

PC

PCPC

PC

PC

XtremWeb-CH at a glance

Job0

Job2

Job1Job3

Application


XtremWeb-CHcoordinator

Communication protocol

Work Request

Worker

Work Result

Work Alive


Use

r’ se

rvic

es

Java

AP

IC

/C+

+ A

PI

Pyt

hon

AP

I

Scheduler

XWCHDB

Work request

Work Result

Warehouse

Worker

Work Alive

Wor

ker

and

War

ehou

se s

ervi

ces

Use

r ap

plic

atio

nXtremWeb-CHCoordinator

Admin. Service


User application (jobs generator)int main ()

{

int Job0, Job1, Job2, Job3

…

Job0 = addjob (…);

while (getJobStatus (Job0,…)) ≠ COMPLETE);

Job0

Job1 = addjob (…);Job2 = addjob (…);while ((getJobStatus (Job1,…)) ≠ COMPLETE) && (getJobStatus (Job2,…)) ≠ COMPLETE));

Job2

Job1

Job3 = addjob (…); while (getJobStatus (Job3,…)) ≠ COMPLETE);

getJobFileOut (Job3,…);}

Job3


User application: waves generator


EZ Grid

Isolates XWCH jobs from “local” jobs

Why?: guarantee the privacy of the providers

How?: use virtualization technology

Supports check-point, and migration

Why?: manage the volatility of nodes

How?: remotely monitor the XWCH virtual machine


Work request

Work Result

Warehouse

Worker

Work Alive

XWCHcoordinator

OS (Windows)

VM Manager

OS (Linux)

XWCH

Application

EZ

Grid

mo

dul

e

OS (Linux)

XWCH

ApplicationL

oca

l app

lica

tion

XWCH worker

XWCH VM1 XWCH VM2

EZ Gridcoordinator


PLAN

• Virtual EZ Grid ingredients

• JOpera

• XtremWeb-CH

• EZ Grid

• Applications


Applications

NeuroWeb : build neuronal maps extracted from brain measurements

MedGift : medical image analysis and retrieval application


NeuroWebObjective

• Reconstruction of the electromagnetic brain map

• Which neuron is responsible of what?— Epileptic crisis (*)

— Parkinson

— Alzheimer

— Etc.

Why?

• Avoid invasive surgeries


Neuronal activity

MEG Scanner

# Sensors = 256dt = 1 millisecond

MEG scannerMagneto-EncephaloGraphy (MEG) scanner:provides temporal information (functional data)

Based on slides by Cédric Bilat


?NeuroWeb

# of sensors: 256

A large scale optimization problem

# of neurons: 60’000



Mapping of the MEG signals

on voxels (IRM):

IRM

MEG

How does the system work?



Start with a random matrix A0

In each step, calculate Ai .

• Ai = F (Ai-1, functional data, anatomic data)

Stop when Ai = Ai-1

~16 Mb

~360 Mb

The Algorithm


.

.

.

.

.

.

.

.

.

…

A0A1 Ai-1

Gridification

Ai


HD

XWCH

Worker

Worker

node

Main Memory

Data persistence

Persistent

server

XWCH

Job

Why ?• Avoid loading/storing data

from/to the HD

How ?• Data remain in main memory

even after the end of the task


Virtual EZ Grid today• Infrastructure: Around 500 workers

HES-SO (Geneva + Yverdon): ~250

UNiGe: ~ 200

UniNe: ~ 20

Univ. Franche Comté (France): ~30

• Reliability: The implementation of EZ Grid is in progress (50%)

• Security: Fully operational. based on SWITCHaai: Shibboleth-based AAI

• Negotiation Model: In progress (30%)

• Applications• NeuroWeb: a prototype (proof-of-concept) is already deployed

• MedGift: gridification will start soon (January 2010)


Swiss Grid Portal

Virtual EZ-GridSwiss Multi-Science

Computing Grid

Infrastructure

Virtual EZ Grid : Links with other projects


Model 1

• Prices are fixed by a “central” agency.

• All the “institutions” receives initial credit which:

• depends on the quantity of resources provided by the institution

• can be used against the usage.

• The client (from a given institution) chooses the priority of jobs. The goal is to:

• Minimize the cost of execution

• Execute the application ASAP

• The model determines the “best” prices which optimize the use of the platform


Model 2

XWCHcoordinator

Worker (1) Proposed price

(2) feedback(3) Price updating

Documents

1 Virtual EZ Grid AAA/Switch project Nabil Abdennadher HES-SO, hepia (Geneva)