WP3 Étude et modélisation de l’ordonnancement et du déploiement des

applications

7/5/2009

WP3: Overview

• Les tâches T3.1: Etude et modélisation T3.2: Mise en œuvre

• Bilan

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Ordonnancement

E. Caron - Réunion #11 - 7/5/09

Ocean-Atmosphere scheduling within DIET

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• Improve performances in a climate prediction application• Modelization of the application• Proof of usage of Grid’5000 and DIET

Scheduling on real application • Scheduling done at two levels

Groups of processors at cluster level Distribution of scenarios at grid level

• Real implementation suffered from technical limitations• Simulations are quite precise but we need to keep one resource

for post-processing tasks

E. Caron - Ocean-Atmosphere scheduling within DIET - APDCT-08

Cluster Level Scheduling – Experimental result

• Experiment: 10 scenarios, 5 clusters, from 11 to 112 resources• Every resource is taken into account• Average makespan is strictly decreasing when adding more

resources• The decrease rate of the average makespan diminishes

E. Caron - Ocean-Atmosphere scheduling within DIET - APDCT-08

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Result Grid Level Scheduling

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• Comparison with Round Robin on 5 clusters• Maximum speedup: 25%• With a higher load, the

algorithm behaves better with a few resources

• Convergence on gains• Gain of 25% ≈ 230h on

a ≈ 822h long experiment

E. Caron - Ocean-Atmosphere scheduling within DIET - APDCT-08

Workflow Management

• Workflow representation Direct Acyclic Graph (DAG)

Each vertex is a task Each directed edge represents communication between

tasks

• Goals Build and execute workflows Use different heuristics to solve scheduling

problems Extensibility to address multi-workflows

submission and large grid platform Manage heterogeneity and variability of

environment

• Research topics addressed Workflow scheduling with parallel tasks Multiple workflows scheduling (makespan

minimization and fairness optimization)

• Specific agent for workflow management (MA DAG)• Two modes:

MA DAG defines a complete scheduling of the workflow (ordering and mapping) MA DAG defines only an ordering for the workflow execution, the mapping is done in the

next step by the client which pass by the Master Agent to find the server where execute the workflow services.

• Design of heuristics for mixed parallelism

Architecture with MA DAG

agent

SeD_parallel

Front-end

NFS

OAR LSF PBS Loadleveler

GLUE

SeD_batch

SeD_seq

Parallel and batch submissions

• Parallel & sequential jobs → transparent for the user

• Submit a parallel job→ system dependent

NFS: copy the code? Numerous batch systems Batch schedulers behaviour

(queues, scripts, etc.) Information about the

internal scheduling process Monitoring

& Performance prediction

Simulation (Simbatch)

agent

SGE

Task reallocation in a grid environment

• Batch simulator: Simbatch [Y. Caniou, J.S. Gay] Validated against OAR (less than 2% error) Based on Simgrid

• Grid simulated by Simgrid with several Simbatch instances• Different algorithms studied: MCT MinMin MaxMin on batchs

using FCFS or CBF - MaxMin and MinMin can not try to reschedule more than 30 jobs at each reallocation (choose oldest or youngest jobs)

• Comparison of jobs completion time with and without reallocation

Task reallocation in a grid environment

• Traces of one month from Grid’5000 (january to june 2008) on three sites

• Reallocation triggered every hour

Batch 1 Batch 2

Meta - SchedulerMeta - Scheduler

Get waiting jobs

Submit Cancel

Reschedule

Déploiement

E. Caron - Réunion #11 - 7/5/09

Deployment Brick: ADAGE

• Automatic deployment tool for grid environment• Only one command to deploy

3 kinds of input information Resource description application description control parameter

• Planning model (random, round-robin), …

• Plug-in for generic application mapping RR, Random, DIET, Graal-heuristics

• Plug-in for each application kind Description convector Configuration of application CCM, MPI, JXTA, P2P, DIET, GFARM, SEQ

Plug-in: from 400 to 4700 C++ lines

META Enable constraints between any other application kinds (at the generic

level)

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Identification of the steps of Automatic Deployment

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MPI Application Description

CCM Application Description

Resource Description

Generic Application Description

Control Parameters

Deployment Planning

Deployment Plan Execution

Application Configuration

Stat

ic

Applic

atio

ns

Deployme

nt Tool

Comparaison GoDIET / Adage

• Déploiement sur Grid’5000 : Entre 25 et 305 nœuds Entre 1 et 8 grappes Heuristique pour créer automatiquement la hiérarchie DIET

Comparaison GoDIET / Adage

• Hiérarchie DIET générée

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ADAGE & LEGO

• Clean implementation of the Adage model UML-like based specifications Separations of planner and application plugins from core

• Extension of the internal generic model (GADe) Support of graph-like generic description

In particular recursive structures like trees (for DIET)

• Support of pseudo-dynamic re-deployment• Support of the G5K API• Working and stable tool

Use to deploy CCM, JuxMem & DIET elements Cf Demonstrator talk

Grid'5000 Reservation Utility for Deployment Grid'5000 Reservation Utility for Deployment UsageUsage

• Web: http://grudu.gforge.inria.fr

GRUDU – Resources Allocation

• We are able to reserve ressources (OAR1 & OAR2) Time parameters, date and reservation walltime Queue OARGrid sub behaviour/ Script to launch

GRUDU – Monitoring

• We are able to monitor the status of the grid/site/a job.• We are able to get instantaneous/historical data with Ganglia

GRUDU - KaDeploy/JFTP

• GUI for KaDeploy jobs deployment• File Transfert interface (local<->remote/rsync on Grid'5000)

WP3: Bilan

• Réalisations principales Délivrable D3.2

ADAGE DIET 2.3

GRUDU• Perspectives

Prise en compte automatique de la plateforme pour le planning Clustering auto-stabilisant

ADAGE ? Utilisation de l’ordonnancement de l’application du CERFACS pour un

modèle régional atmosphérique: CRIP UJF (IMAG. Grenoble) D’autres classes d’applications à ordonnancer

Ordonnancement et gestion de données: création et utilisation de DAGDA

• Collaborations Salomé (EDF) [thèse en cours] Université de Picardie Jules Verne Université du Nevada Las Vegas Université d’Hawaii

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