George Goulas, Christos Gogos, Panayiotis Alefragis, Efthymios Housos

Computer Systems Laboratory,Electrical & Computer Engineering Dept.,

University of Patras

Scheduling Human ResourcesScheduling Problem: Assign a set of ACTIVITIES to

a set of RESOURCES given a set of DEMANDS and a set of CONSTRAINTS to produce a set of PRODUCTS(Goods or services)

Resources include humans (opposed to computer process scheduling)Upfront knowledge of task details

NP-hard/NP-Complete problem, Combinatorial Explosion

Problem Domains: Transportation, bus/driver scheduling, airline crew pairing, course /exams timetabling, Production, job-shop scheduling, ..

SchedScripterSupport the creation of Distributed Human Resources Scheduling

Applications On a grid infrastructure like the EGEEUsing a distributed scheduling application

model Supports dynamic component composition

guided by the solution process and progressUsing open, widely used protocols and

implementations for communications and coordination

Distributed Scheduling AppPre

Processing

Sub Problem Selection

Strategy Selector

Post Processing

Finish Subproble

mFinish

Feedback

Alternative Schedule

Generators

Legality / Regulation Evaluation

Cost Calculation

Optimization / Solution Selection

Scheduling Coordinator

Problem Solver

Different Problem Solvers

SchedScripter FeaturesScheduling Application Templates based on

Distributed Design PatternsApplication Coordination using Application

Templates or BPEL or ad-hoc Web Services usage

Submit web services container as grid jobs on the worker nodes

Application Structure

WORKER

NODE

Application

Code

WORKER

NODE

Application

Code

WORKER

NODE

Application

Code

WORKER

NODE

Application

Code

WORKER

NODE

Application

Code

WORKER

NODE

Application

Code

WORKER

NODE

Application

Code

Registry

Application Coordination

Workflow Mgmt

Bulletin

WN Matcher

Grid

Application Coordination 1/3Master / Worker

CoordinationJava implementation of

the master/worker distributed design pattern

For developers not familiar with WS-*/BPEL

CoordinatorBulletin

ImproverImprover

Improver Host

ImproverImprover

Improver Host

Application Coordination 2/3Swarm Pattern (peer-

to-peer)Java implementation of

p2p design pattern“Master” receives

messages and collects final result when ready

For developers not familiar with WS-*/BPEL

Application Coordination 3/3BPEL based Coordination

WN MatcherHierarchical Pattern

proposed Master BPEL invoking

worker BPEL processes Worker BPEL process on

single WN

Web Services ad-hoc integrationToolkit to assign workload

Master Node

Master Node

RegistryWS

Workflow Mgmt

WS-Security using Grid X509 Certificates

Simple WS Container

WN Matcher

WS

Port Forwardin

gWS

BulletinWS

Scheduling Application Coordinato

rMaster File Manageme

ntWS

It can optionally live inside Bulletin or it

can use its web services interface

SwarmMaster

BPEL Composite Services

User Applications and User Services

Worker Node

Worker Node

Job Mgmt

WS

File Mgmt

WS

WN Mgmt

WS

JobHostWS

WS-Security using Grid X509 Certificates

Simple WS Container

User Define

dWS

Improver Host

MesageBox

Improver

Improver

Improver

Improver

Improver

Improver

Exams Timetabling Case Study*Based on an already available examination

timetabling application from our teamInitial Tests

Independent Improvers Parameter SweepTournament between Improvers, 50% chance to

use best available solution or one of the 2-10 top, synchronous

asynchronousModifiable parameters were the Simulated

Annealing meta-heuristic parameters, used in the application

* Gogos C., Goulas G., Alefragis P. and Housos E. Pursuit of Better Results for the Examination Timetabling Problem Using Grid Resources, 2009, IEEE Symposium on Computational Intelligence in Scheduling (CI-Sched), Nashville, Tennessee, USA, 30 Mar-2 Apr 2009 (to appear).

Results

*Best of 100 sequential runs, ~10 min runtime** Same as sequential, more independent parallel runs*** 1200 sec total runtime, 15 synchronous steps, 30 WNs

Problem Sequential* Param. Sweep**

Tournament***

Dataset 1 4.775 4.699 4.699

Dataset 2 385 385 385

Dataset 3 8.996 8.546 8500

Dataset 4 16.204 15.884 14.879

Dataset 5 2.929 2.823 2.795

Dataset 6 25.740 25.565 25.410

Dataset 7 4.087 3.975 3.884

Dataset 8 7.777 7493 7.440

Case Study ExperienceEasy to use environment

1 week effort to adapt application to SchedScripterApplication refactoring to break it into components

Create initial solutionImprove using parameters, initial solution and

specific configuration of meta-heuristicsRestart with different initial solution but keep

problem in memory (problem loading up to a minute long process)

Significant solution quality improvements with grid

Solution Quality (Cost function) Progress Sample

ConclusionsEasy to use frameworkJava templates for non- BPEL/WS-* -savvy developer

Developer provides “strategy” or “coordinator” and “improver” classes

EGEE compatible but not limited, runs even on cluster

BPEL and web-services support for application coordination

BPEL and web services abstractions and “details” could be hard to understand for developers !EPRs, namespaces, deploy.xml, WSDL files, BPEL

engine artifacts, packaging, deployment, partner links, ..

Future WorkExamination timetabling using scatter search is

almost readyDevelop /adapt various scheduling applications

from different scheduling domainsSeeking non-scheduling scientific computing

applicationsAdapting SchedScripter for GT4/WSRF is

interesting but EGEE middleware compliance is a major choicelargest infrastructure we could have access to at

the moment !