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Seams 2012: Reliability-Driven Dynamic Binding via Feedback Control
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Presentation @ SEAMS2012 - http://filieri.dei.polimi.it/publications/2012-seams

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    Seams 2012: Reliability-Driven Dynamic Binding via Feedback Control Seams 2012: Reliability-Driven Dynamic Binding via Feedback Control Presentation Transcript

    • Reliability-DrivenDynamic Bindingvia Feedback ControlA. Filieri, C. Ghezzi, A. Leva, M. Maggio
    • Motivation Running System 2
    • MotivationUsage profile Running System 2
    • MotivationUsage profile Network Running System 2
    • MotivationUsage profile Network Running System 3rd parties 2
    • MotivationUsage profile Network Running SystemQoS goals 3rd parties 2
    • MotivationUsage profile Network Running SystemQoS goals 3rd parties 2
    • MotivationUsage profile Network Deal with continuous changes Running SystemQoS goals 3rd parties 2
    • SOALogin Shipping CheckOutSearch Logout Buy [Buy more] 3
    • Adaptation via dynamic bindingLogin Shipping CheckOutSearch Logout UPS DHL Buy [Buy more] 4
    • Goal 5
    • GoalMake the system continuously provide desired reliability 5
    • Goal Make the system continuously provide desired reliabilityi.e. the probability of successfully accomplishingthe assigned task 5
    • Goal Make the system continuously provide desired reliabilityi.e. the probability of successfully accomplishingthe assigned task = ¯ 5
    • Goal Make the system continuously provide desired reliabilityi.e. the probability of successfully accomplishingthe assigned task = ¯ ¯ 5
    • Goal Make the system continuously provide desired reliabilityi.e. the probability of successfully accomplishingthe assigned task = ¯ ¯ max ( ) 5
    • State of the art ShippingUPS DHL 6
    • State of the art • Heuristics ShippingUPS DHL • Optimization 6
    • State of the art • Heuristics Shipping Fast, but no guaranteesUPS DHL • Optimization Best decision, but slow 6
    • Our proposalExploit established control theory to getefficient, effective, and scalable dynamic selection 7
    • What’s the modelw S* 8
    • What’s the model S1w S* S2 8
    • What’s the model S1 Sw S* S2 F 8
    • What’s the model r1 S1 Sw 1-r2 S* 1-r1 S2 F r2 8
    • What’s the model r1 S1 S pw 1-r2 S* 1-r1 1-p S2 F r2 8
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Global picture S1 pw S* 1-p S2 p n1,n2, nS,nF Controller 10
    • ControllerReliability of the system: + 11
    • ControllerReliability of the system: +Controller’s goal: 11
    • ControllerReliability of the system: +Controller’s goal: = ¯ + 11
    • ControllerReliability of the system: +Controller’s goal: min( ¯ ) + 11
    • ControllerReliability of the system: +Controller’s goal: min( ¯ ) +Controller’s output: 11
    • How to design the controller? 12
    • How to design the controller?The system has to follow its set point 12
    • How to design the controller?The system has to follow its set pointThe system is not linear 12
    • How to design the controller?The system has to follow its set pointThe system is not linearWhat are the disturbances of the process? 12
    • 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
    • Disturbances 13
    • Disturbances1.00.80.60.40.2 Fluctuation 0 0 5 10 15 20 25 30 35 Time step 13
    • 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
    • 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
    • Fluctuation 16
    • FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ r 16
    • FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ rLinearizing the system around the equilibrium: 16
    • FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ rLinearizing the system around the equilibrium: S1 w S2 16
    • FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ rLinearizing the system around the equilibrium: S1 w S2 Standard PI controller 16
    • Smooth and sharp changesAuto-tuner: decide the configuration of the PI to cope with the given equilibrium 17
    • 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
    • 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
    • Multiple alternatives 18
    • Multiple alternatives C0 p 1-p 18
    • Multiple alternatives C0 p 1-p C0 C0 p 1-p p 1-p 18
    • Multiple alternatives C0 p 1-p Level 1 TsMultiratecontroller C0 C0 p 1-p p 1-p Level 2 Ts/2 18
    • Example C0 p 1-pC0 C0 p 1-p p 1-p 19
    • Example C0 p 1-pC0 C0 p 1-p p 1-p.5 .7 .6 .95 19
    • Example C0 p 1-pGoal: .9 C0 C0 p 1-p p 1-p .5 .7 .6 .95 19
    • Example C0 p 1-pGoal: .9 C0 C0 = . p 1-p p 1-p .5 .7 .6 .95 19
    • Example C0 p 1-pGoal: .9 C0 C0 = . = . . p 1-p p 1-p .5 .7 .6 .95 19
    • Example C0 = . p 1-pGoal: .9 C0 C0 = . = . . p 1-p p 1-p .5 .7 .6 .95 19
    • Validation• Matlab simulation• Java stand-alone• J2EE with Spring and AOP 20
    • Validation 21
    • ConclusionsEffectiveEfficientScalableFormally grounded 22
    • ConclusionsEffectiveEfficientScalableFormally groundedTrade-off reliability/performanceImprove ATBest tree balancingOther quantitative properties 22
    • Try it @Home http://filieri.dei.polimi.it/publications/2012-seams/Partially funded by the European Commission, Programme IDEAS-ERC, Project 227977-SMScom 23
    • Control Equations{ 24
    • Control Equations{n( ) = n( ) r( ) +P( ) · r( ) + w( )r( ) = min{tm , n( )} 24
    • Control Equations{n( ) = n( ) r( ) +P( ) · r( ) + w( )r( ) = min{tm , n( )} ( ) ( )( )= ( ) ( )+ ( ) ( ) 24
    • Control Equations{n( ) = n( ) r( ) +P( ) · r( ) + w( )r( ) = min{tm , n( )} ( ) ( )( )= ( ) ( )+ ( ) ( ) Set point: ¯ 24
    • Fluctuation 25
    • FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ r 25
    • FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ r n( ) = A n( )+Linearized B p( ) + B r( ) system y( ) = C n( ) 25
    • FluctuationEquilibrium n = (n, p, ¯) ¯ ¯ ¯ r n( ) = A n( )+Linearized B p( ) + B r( ) system y( ) = C n( )Z-transform ( )= 25
    • Controller 26
    • ControllerError ( )=¯ ( ) 26
    • ControllerError ( )=¯ ( )Standard PI ( ) = ( )+ ( )· ( ) ( ) = ( )+ · ( ) 26