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Geoffrey De Smet Drools Planner: Planning optimization by example

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Demo Drools Planner TSP example

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Every organization has planning problems.

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What is a planning problem? <ul><li>Complete goals

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With limited resources

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Under constraints </li></ul>

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Hospital bed scheduling <ul><li>Assign each </li><ul><li>Patient </li></ul><li>To </li><ul><li>Bed </li></ul><li>Constraints </li><ul><li>Length of stay

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Room requirements

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Room preferences </li></ul></ul>http://www.flickr.com/photos/markhillary/2227726759/

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Hospital nurse rostering <ul><li>Assign each </li><ul><li>Shift </li></ul><li>To </li><ul><li>Nurse </li></ul><li>Constraints </li><ul><li>Employment contract

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Free time preferences

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Skills </li></ul></ul>http://www.flickr.com/photos/glenpooh/709704564/

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School timetabling <ul><li>Assign each </li><ul><li>Course </li></ul><li>To </li><ul><li>Room

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Timeslot </li></ul><li>Constraints </li><ul><li>No simultaneous courses </li><ul><li>Per room

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Per teacher

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Per student </li></ul></ul></ul>http://www.flickr.com/photos/phelyan/2281095105/

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Airline routing <ul><li>Assign each </li><ul><li>Flight </li></ul><li>To </li><ul><li>Airplane

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Crew </li></ul><li>Constraints </li><ul><li>Airplane/crew depart from where they arrive

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Minimize mileage </li></ul></ul>http://www.flickr.com/photos/yorickr/3674349657/

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Bin packing in the cloud <ul><li>Assign each </li><ul><li>Process </li></ul><li>To </li><ul><li>Server </li></ul><li>Constraints </li><ul><li>Hardware requirements

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Minimize server cost </li></ul></ul>http://www.flickr.com/photos/torkildr/3462607995/

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Bin packing in the cloud <ul><li>Assign each </li><ul><li>Process </li></ul><li>To </li><ul><li>Server </li></ul><li>Constraints </li><ul><li>Hardware requirements

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Minimize server cost </li></ul></ul>http://www.flickr.com/photos/torkildr/3462607995/

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Which processes do we assign to this server?

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How did we find that solution?

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First fit by decreasing size

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First fit by decreasing size

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First fit by decreasing size

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First fit by decreasing size

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Another case

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Try FFD again

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Try FFD again

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Try FFD again

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Try FFD again

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FFD failure

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NP complete

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NP complete <ul><li>No silver bullet known </li><ul><li>Holy grail of computer science </li><ul><li>P == NP </li></ul><li>Probably does not exist </li><ul><li>P != NP </li></ul></ul><li>Root problem of all planning problems </li></ul>http://www.flickr.com/photos/annguyenphotography/3267723713/

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Multiple servers...

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Multiple servers...

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Multiple servers...

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Multiple servers...

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Multiple constraints...

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Multiple constraints...

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Multiple constraints...

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Multiple constraints...

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Organizations rarely optimize planning problems.

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Reuse optimization algorithms?

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<ul><li>Open source (ASL 2.0)

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Regular releases

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Reference manual

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Examples </li></ul>

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Given 2 solutions, which one is better?

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Each Solution has 1 Score

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Each Solution has 1 Score

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Better score => better solution

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Better score => better solution

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Best score => best solution

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Best score => best solution

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Demo Drools Planner CloudBalance example

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Domain model

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Server

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Server public class Server { private int cpuPower ; private int memory ; private int networkBandwidth ; private int cost ; // getters }

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Process

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Process

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Process is a planning entity @PlanningEntity public class Process { private int requiredCpuPower ; private int requiredMemory ; private int requiredNetworkBandwidth ; ... // getters, clone, equals, hashcode }

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Process has a planning variable @PlanningEntity public class Process { ... private Server server ; @PlanningVariable @ValueRange(type = FROM_SOLUTION_PROPERTY, solutionProperty = &quot;serverList&quot; ) public Server getServer() { return server ; } public void setServer(Server server) { ... } ... }

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CloudBalance

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Solution CloudBalance: problem facts public class CloudBalance implements Solution<HardAndSoftScore> { private List<Server> serverList ; public List<Server> getServerList() { return serverList ; } ... }

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Solution CloudBalance: planning entities public class CloudBalance implements Solution<HardAndSoftScore> { ... private List<Process> processList ; @PlanningEntityCollectionProperty public List<Process> getProcessList() { return processList ; } ... }

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Solution CloudBalance: getProblemFacts public class CloudBalance implements Solution<HardAndSoftScore> { ... // Used in score constraints public Collection<Object> getProblemFacts() { List<Object> facts = new ArrayList<Object>(); facts.addAll( serverList ); return facts; } ... }

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Solution CloudBalance: score public class CloudBalance implements Solution<HardAndSoftScore> { ... private HardAndSoftScore score ; public HardAndSoftScore getScore() { ... } public void setScore(HardAndSoftScore score) { ... } ... }

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Score constraints

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Score calculation with Drools rule engine <ul><li>DRL </li><ul><li>Declarative

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Like SQL, regular expressions </li></ul><li>Performance + scalability </li><ul><li>Indexing, ReteOO, ...

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Incremental (delta based) score calculation </li><ul><li>Solution changes => only recalculate part of score </li></ul></ul></ul>

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Soft constraint: server cost rule &quot;serverCost&quot; 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 $c insertLogical( new IntConstraintOccurrence( &quot;serverCost&quot; , ConstraintType. NEGATIVE_SOFT , $c , $s )); end

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Hard constraint: CPU power rule &quot;requiredCpuPowerTotal&quot; 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.intValue() - $cpu) end

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Solving it

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Solver configuration by XML < solver > < solutionClass > ... CloudBalance</ solutionClass > < planningEntityClass > ... Process</> < scoreDrl > ... ScoreRules.drl</ scoreDrl > < scoreDefinition > < scoreDefinitionType >HARD_AND_SOFT</> </ scoreDefinition > <!-- optimization algorithms --> </ solver >

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Solving XmlSolverFactory factory = new XmlSolverFactory( &quot; ... SolverConfig.xml&quot; ); Solver solver = factory .buildSolver(); solver.setPlanningProblem( cloudBalance ); solver.solve(); cloudBalance = ( CloudBalance) solver.getBestSolution();

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Optimization algorithms

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Brute Force

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Brute Force config < solver > ... < bruteForce /> </ solver >

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Brute force scalability 6 processes = 15 ms 9 processes = 1.5 seconds * 100

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Brute force scalability 12 processes = 17 minutes 6 processes = 15 ms 9 processes = 1.5 seconds * 100 * 680

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Plan 1200 processes with brute force?

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First Fit

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First Fit config < solver > ... < constructionHeuristic > < constructionHeuristicType >FIRST_FIT</> </ constructionHeuristic > </ solver >

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First Fit scalability

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First Fit results CPU: OK RAM: OK Network: OK Cost active servers: shown

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First Fit Decreasing

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First Fit Decreasing config < solver > ... < constructionHeuristic > < constructionHeuristicType >FIRST_FIT_DECREASING</> </ constructionHeuristic > </ solver >

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DifficultyComparator public class ProcessDifficultyComparator implements Comparator<Process> { public int compare(Process a, Process b) { // Compare on requiredCpuPower * requiredMemory // * requiredNetworkBandwidth } } @PlanningEntity (difficultyComparatorClass = ProcessDifficultyComparator. class ) public class Process { ... }

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First Fit Decreasing scalability

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First Fit Decreasing results

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Local Search

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Local Search comes after Construction Heuristics < solver > ... < constructionHeuristic > < constructionHeuristicType >FIRST_FIT_DECREASING</> </ constructionHeuristic > < localSearch > ... < localSearch > </ solver >

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Local Search needs to be terminated < solver > ... < termination > < maximumMinutesSpend >20</ maximumMinutesSpend > </ termination > ... </ solver >

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Selecting moves < localSearch > < selector > < selector > < moveFactoryClass >GenericChangeMoveFactory</> </ selector > < selector > < moveFactoryClass >GenericSwapMoveFactory</> </ selector > </ selector > ... tabu search, simulated annealing or ... </ localSearch >

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Hill climbing <ul><li>Select the best improving move

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Gets stuck in local optima </li><ul><li>Not good </li></ul></ul>

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Hill climbing < localSearch > < selector > ... </ selector > < forager > <!-- Untweaked standard value --> < minimalAcceptedSelection >1000</> </ forager > </ localSearch >

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Tabu search <ul><li>Hill climbing ++

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Select the best move </li><ul><li>Recently selected moves are taboo

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Even if the best move is not improving </li></ul><li>Does not get stuck in local optima </li></ul>

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Tabu Search < localSearch > < selector > ... </ selector > < acceptor > <!-- Untweaked standard value --> < propertyTabuSize >7</ propertyTabuSize > </ acceptor > < forager > <!-- Untweaked standard value --> < minimalAcceptedSelection >1000</> </ forager > </ localSearch >

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Simulated annealing <ul><li>Randomly select moves </li><ul><li>Prefer improving moves </li><ul><li>Especially as time goes by </li></ul></ul><li>Does not get stuck in local optima </li></ul>

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Simulated Annealing < localSearch > < selector > ... </ selector > < acceptor > <!-- Tweaked value --> < simulatedAnnealingStartingTemperature > 0hard/400soft</> </ acceptor > < forager > <!-- Untweaked standard value --> < minimalAcceptedSelection >4</> </ forager > </ localSearch >

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Local Search results

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Cost ($) reduction <ul><li>Compared to First Fit </li><ul><li>First Fit Decreasing </li><ul><li>49 480 $ = 4 % </li></ul><li>Tabu Search </li><ul><li>160 860 $ = 14 % </li></ul><li>Simulated annealing </li><ul><li>128 950 $ = 11 % </li></ul></ul><li>Few constraints here </li><ul><li>=> low ROI </li></ul></ul>

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Organizations rarely optimize planning problems.

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http://www.flickr.com/photos/techbirmingham/345897594/ Organizations waste resources.

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Real-time planning

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Real-time paradox <ul><li>First Fit Decreasing 1200 processes = 8 seconds

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Time allowed: 100ms after last change </li></ul>

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Demo Real-time planning CloudBalance example

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Demo Real-time planning TSP example

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Summary

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Summary <ul><li>Drools Planner optimizes planning

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Adding constraints is easy and scalable

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Switching/combining algorithms is easy </li></ul>

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Try an example!

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Q&A <ul><li>Drools Planner homepage: </li><ul><li>http://www.jboss.org/drools/drools-planner </li></ul><li>Reference manual: </li><ul><li>http://www.jboss.org/drools/documentation </li></ul><li>Download this presentation: </li><ul><li>http://www.jboss.org/drools/presentations </li></ul><li>Twitter: @geoffreydesmet

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Google+: Geoffrey De Smet </li></ul>