drools

Geoffrey De Smet

Business resource optimization
with
Drools Planner

DemoTravelling Salesman ProblemVehicle Routing

Every organization has planning problems.

What is a planning problem?

Complete goals

With limited resources

Under constraints

Hospital employee rostering

Assign eachShift

ToEmployee

ConstraintsEmployment contract

Free time preferences

Skills

http://www.flickr.com/photos/glenpooh/709704564/

DemoEmployee rostering

Other planning problems

http://www.flickr.com/photos/phelyan/2281095105/

http://www.flickr.com/photos/markhillary/2227726759/

Hospital bed planning

Course scheduling

Many more: port planning, airline routing, train composition,
task assignment, assembly line planning,
satellite bandwidth planning, stock cutting, ...

Bin packing in the cloud

Assign eachProcess

ToServer

ConstraintsHardware requirements

Minimize server cost

http://www.flickr.com/photos/torkildr/3462607995/

Bin packing in the cloud

Assign eachProcess

ToServer

ConstraintsHardware requirements

Minimize server cost

http://www.flickr.com/photos/torkildr/3462607995/

Which processes do we assign to this server?

How did we find that solution?

First fit by decreasing size

First fit by decreasing size

First fit by decreasing size

First fit by decreasing size

Another case

Try FFD again

Try FFD again

Try FFD again

Try FFD again

FFD failure

NP complete

NP complete

No silver bullet knownHoly grail of computer scienceP == NP

Probably does not existP != NP

Root problem of
all planning problems

http://www.flickr.com/photos/annguyenphotography/3267723713/

Multiple servers...

Multiple servers...

Multiple servers...

Multiple servers...

Multiple constraints...

Multiple constraints...

Multiple constraints...

Multiple constraints...

Organizations rarely optimize
planning problems.

Given 2 solutions, which one is better?

Each Solution has 1 Score

Each Solution has 1 Score

Better score => better solution

Better score => better solution

Best score => best solution

Best score => best solution

Reuse optimization algorithms?

Open source (ASL 2.0)

Regular releases

Reference manual

Examples

DemoCloudBalance example

Domain model

Server

Server

public class Server {

private int cpuPower; private int memory; private int networkBandwidth;

private int cost;

// getters}

Process

Process

Process is a planning entity

@PlanningEntitypublic class Process {

private int requiredCpuPower; private int requiredMemory; private int requiredNetworkBandwidth; private Server server;

@PlanningVariable @ValueRange(... = "serverList") public Server getServer() {...}

// getters, setters, clone, equals, hashcode}

CloudBalance

Solution class CloudBalance

public class CloudBalance implements Solution {

private List serverList; private List processList; private HardAndSoftScore score;

@PlanningEntityCollectionProperty public List getProcessList() {...}

// getters, setters, clone, getProblemFacts()

}

Score constraints

Score calculation

Simple Java

Incremental Java

Drools

Simple Java score calculation

Easy to implement

Bridge to an existing scoring system

Slow

public class ... implements SimpleScoreCalculator {

public Score calculateScore(CloudBalance cb) { ... }

}

Incremental Java
score calculation

FastSolution changes => only score delta is recalculated

Hard to implementMuch boilerplate code

Drools score calculation

Also incremental => fastbut no boilerplate code

Integration opportunitiesGuvnor repository

DSL: natural language constraints

Decision tables

DRL soft constraint:
server cost

rule "serverCost" when // there is a server $s : Server($c : cost) // there is a processes on that server exists Process(server == $s) then // lower soft score by $cend

DRL hard constraint:
CPU power

rule "requiredCpuPowerTotal" when // there is a server $s : Server($cpu : cpuPower) // with too little CPU for its processes $total : Number(intValue > $cpu) from accumulate( Process(server == $s, $requiredCpu : requiredCpuPower), sum($requiredCpu) ) then // lower hard score by ($total - $cpu)end

Solving it

Solver configuration by XML

...CloudBalance ...Process

HARD_AND_SOFT ...ScoreRules.drl

Solving

XmlSolverFactory factory = new XmlSolverFactory( "...SolverConfig.xml");Solver solver = factory.buildSolver();

solver.setPlanningProblem(cloudBalance);solver.solve();cloudBalance = (CloudBalance) solver.getBestSolution();

Optimization algorithms

Brute Force

Brute Force config

...

Brute force scalability

12 processes = 17 minutes

6 processes = 15 ms

9 processes = 1.5 seconds

* 100

* 680

Plan 1200 processes
with brute force?

First Fit

First Fit config

...

FIRST_FIT

First Fit Decreasing

First Fit Decreasing config

...

FIRST_FIT_DECREASING

@PlanningEntity(difficultyComparatorClass = ProcessDifficultyComparator.class)public class Process { ...}

First Fit scalability

Local Search comes after
Construction Heuristics

Local Search comes after
Construction Heuristics

...

FIRST_FIT_DECREASING ...

Local Search

Local Search Algorithms

(Hill climbing)

Tabu Search

Simulated Annealing

Late Acceptance

Details explained Friday 4:15 PM

Local Search results

Cost ($) reduction

Compared to First FitFirst Fit Decreasing49 480 $ = 4 %

Tabu Search160 860 $ = 14 %

Simulated annealing128 950 $ = 11 %

Few constraints=> low ROI

Benchmarker toolkit

Organizations rarely optimize
planning problems.

http://www.flickr.com/photos/techbirmingham/345897594/

Organizations waste resources.

Summary

Summary

Drools Planner optimizes business resources

Adding constraints is easy and scalable

Switching/combining algorithms is easy

Try an example!

Q&A

Drools Planner homepage:http://www.jboss.org/drools/drools-planner

Reference manual:http://www.jboss.org/drools/documentation

Download this presentation:http://www.jboss.org/drools/presentations

Twitter: @geoffreydesmet

Google+: Geoffrey De Smet

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