Seams 2012: Reliability-Driven Dynamic Binding via Feedback Control

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Seams 2012: Reliability-Driven Dynamic Binding via Feedback Control

  1. 1. Reliability-DrivenDynamic Bindingvia Feedback ControlA. Filieri, C. Ghezzi, A. Leva, M. Maggio
  2. 2. Motivation Running System 2
  3. 3. MotivationUsage profile Running System 2
  4. 4. MotivationUsage profile Network Running System 2
  5. 5. MotivationUsage profile Network Running System 3rd parties 2
  6. 6. MotivationUsage profile Network Running SystemQoS goals 3rd parties 2
  7. 7. MotivationUsage profile Network Running SystemQoS goals 3rd parties 2
  8. 8. MotivationUsage profile Network Deal with continuous changes Running SystemQoS goals 3rd parties 2
  9. 9. SOALogin Shipping CheckOutSearch Logout Buy [Buy more] 3
  10. 10. Adaptation via dynamic bindingLogin Shipping CheckOutSearch Logout UPS DHL Buy [Buy more] 4
  11. 11. Goal 5
  12. 12. GoalMake the system continuously provide desired reliability 5
  13. 13. Goal Make the system continuously provide desired reliabilityi.e. the probability of successfully accomplishingthe assigned task 5
  14. 14. Goal Make the system continuously provide desired reliabilityi.e. the probability of successfully accomplishingthe assigned task = ¯ 5
  15. 15. Goal Make the system continuously provide desired reliabilityi.e. the probability of successfully accomplishingthe assigned task = ¯ ¯ 5
  16. 16. Goal Make the system continuously provide desired reliabilityi.e. the probability of successfully accomplishingthe assigned task = ¯ ¯ max ( ) 5
  17. 17. State of the art ShippingUPS DHL 6
  18. 18. State of the art • Heuristics ShippingUPS DHL • Optimization 6
  19. 19. State of the art • Heuristics Shipping Fast, but no guaranteesUPS DHL • Optimization Best decision, but slow 6
  20. 20. Our proposalExploit established control theory to getefficient, effective, and scalable dynamic selection 7
  21. 21. What’s the modelw S* 8
  22. 22. What’s the model S1w S* S2 8
  23. 23. What’s the model S1 Sw S* S2 F 8
  24. 24. What’s the model r1 S1 Sw 1-r2 S* 1-r1 S2 F r2 8
  25. 25. What’s the model r1 S1 S pw 1-r2 S* 1-r1 1-p S2 F r2 8
  26. 26. What’s the model r1(k) S1 S p(k)w(k) 1-r2(k) S* 1-r1(k) 1-p(k) S2 F r2(k) 9
  27. 27. What’s the model r1(k) S1 S p(k) w(k) 1-r2(k) S* 1-r1(k) 1-p(k) S2 F r2(k)Sampling time: Ts 9
  28. 28. What’s the model n1(k) r1(k) S1 S n*(k) p(k) w(k) 1-r2(k) S* n2(k) 1-r1(k) 1-p(k) S2 F r2(k)Sampling time: Ts 9
  29. 29. What’s the model n1(k) , R1 r1(k) S1 S n*(k), R* p(k) w(k) 1-r2(k) S* n2(k) , R2 1-r1(k) 1-p(k) S2 F r2(k)Sampling time: Ts 9
  30. 30. What’s the model n1(k) , R1 r1(k) S1 S n*(k), R* p(k) w(k) 1-r2(k) S* n2(k) , R2 1-r1(k) 1-p(k) S2 F r2(k)Sampling time: Ts 9
  31. 31. What’s the model n1(k) , R1 nS(k) r1(k) S1 S n*(k), R* p(k) w(k) 1-r2(k) S* n2(k) , R2 1-r1(k) nF(k) 1-p(k) S2 F r2(k)Sampling time: Ts 9
  32. 32. What’s the model n1(k) R1 , nS(k) r1(k) S1 S n*(k) R* , p(k) w(k) 1-r2(k) S* n2(k), R2 nF(k) 1-r1(k) 1-p(k) S2 F r2(k)Sampling time: Ts 9
  33. 33. What’s the model n1(k) , R1 nS(k) r1(k) S1 S n*(k), R* p(k) w(k) 1-r2(k) S* n2(k) , R2 1-r1(k) nF(k) 1-p(k) S2 F r2(k)Sampling time: Ts 9
  34. 34. What’s the model n1(k) , R1 nS(k) r1(k) S1 S n*(k), R* p(k) w(k) 1-r2(k) S* n2(k) , R2 1-r1(k) nF(k) 1-p(k) S2 F r2(k)Sampling time: Ts 9
  35. 35. Global picture S1 pw S* 1-p S2 p n1,n2, nS,nF Controller 10
  36. 36. ControllerReliability of the system: + 11
  37. 37. ControllerReliability of the system: +Controller’s goal: 11
  38. 38. ControllerReliability of the system: +Controller’s goal: = ¯ + 11
  39. 39. ControllerReliability of the system: +Controller’s goal: min( ¯ ) + 11
  40. 40. ControllerReliability of the system: +Controller’s goal: min( ¯ ) +Controller’s output: 11
  41. 41. How to design the controller? 12
  42. 42. How to design the controller?The system has to follow its set point 12
  43. 43. How to design the controller?The system has to follow its set pointThe system is not linear 12
  44. 44. How to design the controller?The system has to follow its set pointThe system is not linearWhat are the disturbances of the process? 12
  45. 45. How to design the controller? The system has to follow its set point The system is not linearWhat are the disturbances of the process? 12
  46. 46. Disturbances 13
  47. 47. Disturbances1.00.80.60.40.2 Fluctuation 0 0 5 10 15 20 25 30 35 Time step 13
  48. 48. Disturbances1.00.80.60.40.2 Smooth 0 0 5 10 15 20 25 30 35 Time step 1.0 0.8 0.6 0.4 0.2 Fluctuation 0 0 5 10 15 20 25 30 35 Time step 14
  49. 49. Disturbances1.00.80.60.40.2 Sharp 0 0 5 10 15 20 25 30 35 Time step 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.2 Fluctuation 0.4 0.2 Smooth 0 0 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 35 Time step Time step 15
  50. 50. Fluctuation 16
  51. 51. FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ r 16
  52. 52. FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ rLinearizing the system around the equilibrium: 16
  53. 53. FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ rLinearizing the system around the equilibrium: S1 w S2 16
  54. 54. FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ rLinearizing the system around the equilibrium: S1 w S2 Standard PI controller 16
  55. 55. Smooth and sharp changesAuto-tuner: decide the configuration of the PI to cope with the given equilibrium 17
  56. 56. Smooth and sharp changesAuto-tuner: decide the configuration of the PI to cope with the given equilibrium Trade-off between responsiveness and overshooting avoidance 17
  57. 57. Smooth and sharp changesAuto-tuner: decide the configuration of the PI to cope with the given equilibrium Trade-off between responsiveness and overshooting avoidance Limitation: the goal has to be feasible 17
  58. 58. Multiple alternatives 18
  59. 59. Multiple alternatives C0 p 1-p 18
  60. 60. Multiple alternatives C0 p 1-p C0 C0 p 1-p p 1-p 18
  61. 61. Multiple alternatives C0 p 1-p Level 1 TsMultiratecontroller C0 C0 p 1-p p 1-p Level 2 Ts/2 18
  62. 62. Example C0 p 1-pC0 C0 p 1-p p 1-p 19
  63. 63. Example C0 p 1-pC0 C0 p 1-p p 1-p.5 .7 .6 .95 19
  64. 64. Example C0 p 1-pGoal: .9 C0 C0 p 1-p p 1-p .5 .7 .6 .95 19
  65. 65. Example C0 p 1-pGoal: .9 C0 C0 = . p 1-p p 1-p .5 .7 .6 .95 19
  66. 66. Example C0 p 1-pGoal: .9 C0 C0 = . = . . p 1-p p 1-p .5 .7 .6 .95 19
  67. 67. Example C0 = . p 1-pGoal: .9 C0 C0 = . = . . p 1-p p 1-p .5 .7 .6 .95 19
  68. 68. Validation• Matlab simulation• Java stand-alone• J2EE with Spring and AOP 20
  69. 69. Validation 21
  70. 70. ConclusionsEffectiveEfficientScalableFormally grounded 22
  71. 71. ConclusionsEffectiveEfficientScalableFormally groundedTrade-off reliability/performanceImprove ATBest tree balancingOther quantitative properties 22
  72. 72. Try it @Home http://filieri.dei.polimi.it/publications/2012-seams/Partially funded by the European Commission, Programme IDEAS-ERC, Project 227977-SMScom 23
  73. 73. Control Equations{ 24
  74. 74. Control Equations{n( ) = n( ) r( ) +P( ) · r( ) + w( )r( ) = min{tm , n( )} 24
  75. 75. Control Equations{n( ) = n( ) r( ) +P( ) · r( ) + w( )r( ) = min{tm , n( )} ( ) ( )( )= ( ) ( )+ ( ) ( ) 24
  76. 76. Control Equations{n( ) = n( ) r( ) +P( ) · r( ) + w( )r( ) = min{tm , n( )} ( ) ( )( )= ( ) ( )+ ( ) ( ) Set point: ¯ 24
  77. 77. Fluctuation 25
  78. 78. FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ r 25
  79. 79. FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ r n( ) = A n( )+Linearized B p( ) + B r( ) system y( ) = C n( ) 25
  80. 80. FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ r n( ) = A n( )+Linearized B p( ) + B r( ) system y( ) = C n( )Z-transform ( )= 25
  81. 81. Controller 26
  82. 82. ControllerError ( )=¯ ( ) 26
  83. 83. ControllerError ( )=¯ ( )Standard PI ( ) = ( )+ ( )· ( ) ( ) = ( )+ · ( ) 26

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