Presentation @ SEAMS2012 - http://filieri.dei.polimi.it/publications/2012-seams

1. Reliability-Driven
Dynamic Binding
via Feedback Control
A. Filieri, C. Ghezzi, A. Leva, M. Maggio

2. Motivation
Running System
2

3. Motivation
Usage profile
Running System
2

4. Motivation
Usage profile Network
Running System
2

5. Motivation
Usage profile Network
Running System
3rd parties
2

6. Motivation
Usage profile Network
Running System
QoS goals 3rd parties
2

7. Motivation
Usage profile Network
Running System
QoS goals 3rd parties
2

8. Motivation
Usage profile Network
Deal with
continuous changes
Running System
QoS goals 3rd parties
2

9. SOA
Login Shipping CheckOut
Search Logout
Buy
[Buy more]
3

10. Adaptation via dynamic
binding
Login Shipping CheckOut
Search Logout
UPS DHL
Buy
[Buy more]
4

11. Goal
5

12. Goal
Make the system continuously
provide desired reliability
5

13. Goal
Make the system continuously
provide desired reliability
i.e. the probability of successfully accomplishing
the assigned task
5

14. Goal
Make the system continuously
provide desired reliability
i.e. the probability of successfully accomplishing
the assigned task
= ¯
5

15. Goal
Make the system continuously
provide desired reliability
i.e. the probability of successfully accomplishing
the assigned task
= ¯
¯
5

16. Goal
Make the system continuously
provide desired reliability
i.e. the probability of successfully accomplishing
the assigned task
= ¯
¯ max ( )
5

17. State of the art
Shipping
UPS DHL
6

18. State of the art
• Heuristics
Shipping
UPS DHL
• Optimization
6

19. State of the art
• Heuristics
Shipping Fast, but no guarantees
UPS DHL
• Optimization
Best decision, but slow
6

20. Our proposal
Exploit established control theory to get
efficient, effective, and scalable
dynamic selection
7

21. What’s the model
w
S*
8

22. What’s the model
S1
w
S*
S2
8

23. What’s the model
S1 S
w
S*
S2 F
8

24. What’s the model
r1
S1 S
w 1-r2
S*
1-r1
S2 F
r2
8

25. What’s the model
r1
S1 S
p
w 1-r2
S*
1-r1
1-p
S2 F
r2
8

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. 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. 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. 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. 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. 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. 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. 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. 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. Global picture
S1
p
w
S*
1-p
S2
p n1,n2,
nS,nF
Controller
10

36. Controller
Reliability of the system:
+
11

37. Controller
Reliability of the system:
+
Controller’s goal:
11

38. Controller
Reliability of the system:
+
Controller’s goal:
= ¯
+
11

39. Controller
Reliability of the system:
+
Controller’s goal:
min( ¯ )
+
11

40. Controller
Reliability of the system:
+
Controller’s goal:
min( ¯ )
+
Controller’s output:
11

41. How to design the
controller?
12

42. How to design the
controller?
The system has to follow its set point
12

43. How to design the
controller?
The system has to follow its set point
The system is not linear
12

44. How to design the
controller?
The system has to follow its set point
The system is not linear
What are the disturbances of the process?
12

45. How to design the
controller?
The system has to follow its set point
The system is not linear
What are the disturbances of the process?
12

46. Disturbances
13

47. Disturbances
1.0
0.8
0.6
0.4
0.2 Fluctuation
0
0 5 10 15 20 25 30 35
Time step
13

48. Disturbances
1.0
0.8
0.6
0.4
0.2 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. Disturbances
1.0
0.8
0.6
0.4
0.2 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. Fluctuation
16

51. Fluctuation
Equilibrium n = (n, p, ¯)
¯ ¯ ¯ r
16

52. Fluctuation
Equilibrium n = (n, p, ¯)
¯ ¯ ¯ r
Linearizing the system around the equilibrium:
16

53. Fluctuation
Equilibrium n = (n, p, ¯)
¯ ¯ ¯ r
Linearizing the system around the equilibrium:
S1
w
S2
16

54. Fluctuation
Equilibrium n = (n, p, ¯)
¯ ¯ ¯ r
Linearizing the system around the equilibrium:
S1
w
S2
Standard PI controller
16

55. Smooth and sharp
changes
Auto-tuner: decide the configuration of the PI
to cope with the given equilibrium
17

56. Smooth and sharp
changes
Auto-tuner: decide the configuration of the PI
to cope with the given equilibrium
Trade-off between responsiveness
and overshooting avoidance
17

57. Smooth and sharp
changes
Auto-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. Multiple alternatives
18

59. Multiple alternatives
C0
p 1-p
18

60. Multiple alternatives
C0
p 1-p
C0 C0
p 1-p p 1-p
18

61. Multiple alternatives
C0
p 1-p Level 1
Ts
Multirate
controller
C0 C0
p 1-p p 1-p Level 2
Ts/2
18

62. Example
C0
p 1-p
C0 C0
p 1-p p 1-p
19

63. Example
C0
p 1-p
C0 C0
p 1-p p 1-p
.5 .7 .6 .95 19

64. Example
C0
p 1-p
Goal: .9
C0 C0
p 1-p p 1-p
.5 .7 .6 .95 19

65. Example
C0
p 1-p
Goal: .9
C0 C0
= .
p 1-p p 1-p
.5 .7 .6 .95 19

66. Example
C0
p 1-p
Goal: .9
C0 C0
= . = . .
p 1-p p 1-p
.5 .7 .6 .95 19

67. Example
C0
= .
p 1-p
Goal: .9
C0 C0
= . = . .
p 1-p p 1-p
.5 .7 .6 .95 19

68. Validation
• Matlab simulation
• Java stand-alone
• J2EE with Spring and AOP
20

69. Validation
21

70. Conclusions
Effective
Efficient
Scalable
Formally grounded
22

71. Conclusions
Effective
Efficient
Scalable
Formally grounded
Trade-off reliability/performance
Improve AT
Best tree balancing
Other quantitative properties
22

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. Control Equations
{ 24

74. Control Equations
{
n( ) = n( ) r( )
+P( ) · r( ) + w( )
r( ) = min{tm , n( )}
24

75. Control Equations
{
n( ) = n( ) r( )
+P( ) · r( ) + w( )
r( ) = min{tm , n( )}
( ) ( )
( )=
( ) ( )+ ( ) ( )
24

76. Control Equations
{
n( ) = n( ) r( )
+P( ) · r( ) + w( )
r( ) = min{tm , n( )}
( ) ( )
( )=
( ) ( )+ ( ) ( )
Set point: ¯
24

77. Fluctuation
25

78. Fluctuation
Equilibrium n = (n, p, ¯)
¯ ¯ ¯ r
25

79. Fluctuation
Equilibrium n = (n, p, ¯)
¯ ¯ ¯ r
n( ) = A n( )+
Linearized B p( ) + B r( )
system y( ) = C n( )
25

80. Fluctuation
Equilibrium n = (n, p, ¯)
¯ ¯ ¯ r
n( ) = A n( )+
Linearized B p( ) + B r( )
system y( ) = C n( )
Z-transform ( )=
25

81. Controller
26

82. Controller
Error ( )=¯ ( )
26

83. Controller
Error ( )=¯ ( )
Standard PI
( ) = ( )+ ( )· ( )
( ) = ( )+ · ( )
26