1. Drools Planner:Planning optimization by exampleGeoffrey De
Smet
2. DemoDrools PlannerTSP example 2 3. Every organization has
planning problems.3 4. What is a planning problem? Complete goals
With limited resources Under constraints 4 5. Hospital bed
scheduling Assign each Patient To Bed
Constraintshttp://www.flickr.com/photos/markhillary/2227726759/
Length of stay Room requirements Room preferences5 6. Hospital
nurse rostering Assign each Shift To Nurse
Constraintshttp://www.flickr.com/photos/glenpooh/709704564/
Employmentcontract Free timepreferences Skills6 7. School
timetabling Assign each Course To Room
Timeslothttp://www.flickr.com/photos/phelyan/2281095105/
Constraints No simultaneous courses Per room Per teacher Per
student 7 8. Airline routing Assign each Flight To Airplane
Crewhttp://www.flickr.com/photos/yorickr/3674349657/ Constraints
Airplane/crew depart from where they arrive Minimize mileage 8 9.
Bin packing in the cloud Assign each Process To Server Constraints
Hardwarehttp://www.flickr.com/photos/torkildr/3462607995/requirements
Minimize server cost9 10. Bin packing in the cloud Assign each
Process To Server Constraints
Hardwarehttp://www.flickr.com/photos/torkildr/3462607995/requirements
Minimize server cost10 11. Which processes do we assignto this
server? 11 12. How did we find that solution? 12 13. First fit by
decreasing size 13 14. First fit by decreasing size 14 15. First
fit by decreasing size 15 16. First fit by decreasing size 16 17.
Another case 17 18. Try FFD again18 19. Try FFD again19 20. Try FFD
again20 21. Try FFD again21 22. FFD failure22 23. NP complete23 24.
NP complete No silver bullet known Holy grail of computer science P
== NP Probably does notexist P != NP Root problem
ofhttp://www.flickr.com/photos/annguyenphotography/3267723713/all
planningproblems24 25. Multiple servers...25 26. Multiple
servers...26 27. Multiple servers...27 28. Multiple servers...28
29. Multiple constraints...29 30. Multiple constraints...30 31.
Multiple constraints...31 32. Multiple constraints...32 33.
Organizations rarely optimizeplanning problems.33 34. Reuse
optimization algorithms? 34 35. Open source (ASL 2.0) Regular
releases Reference manual Examples35 36. Given 2 solutions, which
one is better?36 37. Each Solution has 1 Score37 38. Each Solution
has 1 Score38 39. Better score => better solution39 40. Better
score => better solution40 41. Best score => best solution41
42. Best score => best solution42 43. DemoDrools
PlannerCloudBalance example 43 44. Domain model 45. Server 45 46.
Serverpublic class Server {private int cpuPower;private int
memory;private int networkBandwidth;private int cost;// getters}46
47. Process47 48. Process48 49. Process is a planning
entity@PlanningEntitypublic class Process {private int
requiredCpuPower;private int requiredMemory;private int
requiredNetworkBandwidth;...// getters, clone, equals, hashcode}49
50. Process has a planning variable@PlanningEntitypublic class
Process {...private Server server;@PlanningVariable@ValueRange(type
= FROM_SOLUTION_PROPERTY,solutionProperty = "serverList")public
Server getServer() {return server;}public void setServer(Server
server) {...}...} 50 51. CloudBalance 51 52. Solution
CloudBalance:problem factspublic class CloudBalanceimplements
Solution {private List serverList;public List getServerList()
{return serverList;}...}52 53. Solution CloudBalance:planning
entitiespublic class CloudBalanceimplements Solution {...private
List processList;@PlanningEntityCollectionPropertypublic List
getProcessList() {return processList;}...}53 54. Solution
CloudBalance:getProblemFactspublic class CloudBalanceimplements
Solution {...// Used in score constraintspublic Collection
getProblemFacts() {List facts = new
ArrayList();facts.addAll(serverList);return facts;}...}54 55.
Solution CloudBalance:scorepublic class CloudBalance implements
Solution {...private HardAndSoftScore score;public HardAndSoftScore
getScore() {...}public void setScore(HardAndSoftScore score)
{...}...} 55 56. Score constraints 57. Score calculation with
Drools rule engine DRL Declarative Like SQL, regular expressions
Performance + scalability Indexing, ReteOO, ... Incremental (delta
based) score calculation Solution changes => only recalculate
part ofscore57 58. Soft constraint:server costrule "serverCost"
when// there is a server$s : Server($c : cost)// there is a
processes on that serverexists Process(server == $s) then// lower
soft score by $cinsertLogical(new
IntConstraintOccurrence("serverCost",
ConstraintType.NEGATIVE_SOFT,$c,$s));end58 59. Hard constraint: CPU
powerrule "requiredCpuPowerTotal" when// there is a server$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.intValue() - $cpu)end59 60. Solving it 61. Solver
configuration by
XML...CloudBalance...Process>...ScoreRules.drlHARD_AND_SOFT>
61 62. SolvingXmlSolverFactory factory = new XmlSolverFactory(
"...SolverConfig.xml");Solver solver =
factory.buildSolver();solver.setPlanningProblem(cloudBalance);solver.solve();cloudBalance
= (CloudBalance)solver.getBestSolution(); 62 63. Optimization
algorithms 64. Brute Force64 65. Brute Force config... 65 66. Brute
force scalability 9 processes = 1.5 seconds * 100 6 processes = 15
ms 66 67. Brute force scalability 12 processes = 17 minutes * 680 9
processes = 1.5 seconds6 processes = 15 ms * 100 67 68. Plan 1200
processeswith brute force?68 69. First Fit69 70. First Fit
config...FIRST_FIT> 70 71. First Fit scalability71 72. First Fit
resultsCPU: OKRAM: OKNetwork: OKCost active servers: shown 72 73.
First Fit Decreasing 73 74. First Fit Decreasing
config...FIRST_FIT_DECREASING>74 75. DifficultyComparatorpublic
class ProcessDifficultyComparatorimplements Comparator {public int
compare(Process a, Process b) {// Compare on requiredCpuPower *
requiredMemory//*
requiredNetworkBandwidth}}@PlanningEntity(difficultyComparatorClass=
ProcessDifficultyComparator.class)public class Process {...}75 76.
First Fit Decreasing scalability 76 77. First Fit Decreasing
results 77 78. Local Search 78 79. Local Search comes
afterConstruction Heuristics...FIRST_FIT_DECREASING> ...79 80.
Local Search needs to beterminated...20... 80 81. Selecting
movesGenericChangeMoveFactory> GenericSwapMoveFactory> ...
tabu search, simulated annealing or ... 81 82. Hill climbing Select
the best improving move Gets stuck in local optima Not good 82 83.
Hill climbing...1000> 83 84. Tabu search Hill climbing ++ Select
the best move Recently selected moves are taboo Even if the best
move is not improving Does not get stuck in local optima84 85. Tabu
Search...71000>85 86. Simulated annealing Randomly select moves
Prefer improving moves Especially as time goes by Does not get
stuck in local optima86 87. Simulated Annealing...
0hard/400soft>4> 87 88. Local Search results 88 89. Cost ($)
reduction Compared to First Fit First Fit Decreasing 49 480 $ = 4 %
Tabu Search 160 860 $ = 14 % Simulated annealing 128 950 $ = 11 %
Few constraints here => low ROI 89 90. Organizations rarely
optimizeplanning problems.90 91.
http://www.flickr.com/photos/techbirmingham/345897594/Organizations
waste resources. 91 92. Real-time planning 93. Real-time paradox
First Fit Decreasing 1200 processes = 8 seconds Time allowed: 100ms
after last change93 94. DemoReal-time planningCloudBalance example
94 95. DemoReal-time planningTSP example 95 96. Summary 97. Summary
Drools Planner optimizes planning Adding constraints is easy and
scalable Switching/combining algorithms is easy97 98. Try an
example!98 99. 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 99
