performance-responsive scheduling for grid computing dr stephen jarvis high performance systems...

Performance-responsive Scheduling for Grid Computing

Dr Stephen JarvisHigh Performance Systems Group

University of Warwick, UK

High Performance Systems Group

Context• Funded by / collaborating with

– UK e-Science Core Programme– IBM (Watson, Hursley)– NASA (Ames)– NEC Europe– Los Alamos National Laboratory

• Integrate established performance tools into emerging grid middleware


What do we mean by ‘scheduling’• Users view

– Jobs run somewhere on the Grid– Notion of deadline– Execution is single domain (includes pre-staging)

• Resource providers view– Don’t mind which jobs are run where– As long as resources are well/evenly used– Maintaining customers deadlines is important

• System view– Jobs can run anywhere– Resources are heterogeneous– Throughput is important, as are scheduling overheads


Managing through Middleware


•Determine what resources are required (predict)

•Determine what resources are available (discover)

•Map requirements to available resources (schedule)

•Maintain contract of performance (QoS)

Managing through Middleware

Performance Services• Intra-domain

– Lab- / department-based– Shared resources under

local administration

• Multi-domain– Campus- / country-based– Wide-area resource and

task management– Cross domain


Performance ServicesHigh Performance Systems Group

• Intra-domain– Lab- / department-based– Shared resources under

local administration

• Multi-domain– Campus- / country-based– Wide-area resource and

task management– Cross domain

Performance Prediction• Performance prediction tools• Aim to predict

– Execution time– Communication usage– Data and resource requirements

• Provides best guess as to how an application will execute on a given resource



PACE User

Application

Resource


PACE User

Application

Resource

ApplicationModel

Resource Model

Application

ApplicationModel

Resource

Resource Model

PACE User

Evaluation Engine

Model parameters

Resource config.


Why is prediction useful?• Scaling properties• Compare runtime

options with– deadline– available resources– priority / other jobs– etc.


05

101520253035404550

1 4 7 10 13 16

The Number of Processors

Runn

ing

Tim

e on

SG

IOrig

in20

00 (s

ec)

sweep3d

fft

improc

closure

jacobi

memsort

cpi

Allows runtime scenarios to be explored before deployment

Run

-tim

e

1. Intra-Domain Co-Scheduling


• Augment Condor scheduler with additional performance information

• Scheduler driver, or co-scheduler (called Titan)

• Use predictive data for system improvement– Time to complete tasks / utilisation of resources– QoS – ability to meet deadlines

• Handle predictive and non-predictive tasks

Intra-Domain Co-SchedulingHigh Performance Systems Group

• Non-predictive tasks

PORTALPRE-

EXECUTIONENGINE MATCHMAKER

SCHEDULEQUEUE

PACE

GA CLUSTERCONNECTOR

CONDOR

REQUESTS FROM USERS OR OTHERDOMAIN SCHEDULERS

RESOURCES

CLASSADS

Titan

Intra-Domain Co-SchedulingHigh Performance Systems Group

• Non-predictive tasks• Tasks with prediction

dataPORTAL

PRE-EXECUTION

ENGINE MATCHMAKER

SCHEDULEQUEUE

PACE

GA CLUSTERCONNECTOR

CONDOR

REQUESTS FROM USERS OR OTHERDOMAIN SCHEDULERS

RESOURCES

CLASSADS

Titan

Intra-Domain DeploymentWithout co-scheduler With co-scheduler

Time to complete = 70.08m Time to complete = 35.19m


• Publish intra-domain perf. data through global information services (MDS)

• Augment service with agent system– One agent per domain / VO

• When a task is submitted– Agents query IS, and negotiate to discover best

domain to run task• Scheme is tested on a 256-node exp. Grid

– 16 resource domains; 6 arch. types


2. Multi-Domain Management


Multi-Domain Management

time


Multi-Domain Management

Time to complete = 2752s

Multi-Domain Management High Performance Systems Group

Time to complete = 467s; an improvement of 83%

QoS: Ability to Meet DeadlineHigh Performance Systems Group

active inactive

Resource usageHigh Performance Systems Group

active inactive

Other work• OGSA compatibility• Prediction

– Accuracy– Other prediction techniques

• Workflow (CCGrid 2003)• Reservation• V. 1.1, Condor/GT2-based

– www.dcs.warwick.ac.uk/~hpsg– Documented at HPDC-12/GGF-8, FGCS


performance-responsive scheduling for grid computing dr stephen jarvis high performance systems...

Documents