These are the slides I presented in FACS'11 regarding this accepted paper (http://goo.gl/SKPga). Normally, when designing a distributed system, you have to develop a choreograpy or orchestration able to coordinate the different services. This process is called synthesis and it is usually done at design time and it entails an exponential complexity. In this work, we developed dynamic learning adaptors that don't require the synthesis, it is a lightweight algorithm and is able to adapt to sporadic errors and changes in the system

1. Learning from Failures:
a Lightweight Approach to
Run-Time Behavioural Adaptation
J. Antonio Martín, Antonio Borgi and Ernesto Pimentel
University of Málaga - University of Pisa
14 of September, 2011 Oslo, Norway FACS'11

2. Motivation
In pervasive computing scenarios, we have thousands of
interacting entities with limited resources which may move,
change and degrade over time

3. Motivation
Existing WSAN could benefit from other networks by
collaborating in communications and functionality

4. Motivation
Flaky communication channels or low battery could disable part of
the functionality of a node. Therefore, other nodes should avoid
wasting battery requesting such functionality

5. Motivation
Changes such as node replacement, hardware malfunctions and
environment interferences should also be adapted
Failures occur, but they must be mitigated or solved altogether

6. Assumptions
• We do not know the actual behaviour of the services
o New unforeseen services might come up
o Service behaviour may change unexpectedly due to
hardware issues
o This causes incompatibilities in signature and behaviour
• The nodes have limited resources
o Computation, memory and battery are scarce
o Communications quickly deplete the battery
o Bounded services
• We have an adaptation contract
o This is based on the signature of the services
o And (optionally) a declarative representation of the goal of
the adapted session
o Alternatively, we could use zero-knowledge adaptation

7. Behavioural adaptation
Different operation names or operations which are require in a
different order than expected cause deadlock situations and
impede the correct interaction among services

8. Behavioural adaptation
An adaptor intermediates between two parts of the system while
solving their incompatibilities in signature and behaviour
For instance, an adaptor can wrap an incompatible service

9. Behavioural adaptation
An adaptor is specified in a declarative maner
by an adaptation contract
Traditionally, adaptors are synthesised knowing the contract
and the behaviour of the services

10. Learning adaptors
In this work, however, because of the ever-changing nature of
pervasive computing scenarios, we don't know the behaviour
of the services, we cannot synthesise the adaptor and,
therefore, we have to learn their incompatibilities dynamically

11. Learning adaptors
Basically, the adaptor will offer every interaction allowed by the
contract and, when deadlock situations arise, the adaptor learns
to avoid that failed trace henceforth. Therefore, failed traces
might occur but the adaptor eventually learns to avoid them

12. Adaptation contract
Services Adaptation Contract Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
...
!user(usr) 8. <> !quit() ?login(usr,
!pass(psw) 9. !quit() <> psw)
... ...
!quit() !quit()

13. Adaptation contract
Services Adaptation Contract Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
...
!user(usr) 8. <> !quit() ?login(usr,
!pass(psw) 9. !quit() <> psw)
... ...
!quit() !quit()

14. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <> 1. Adaptation contract
2. !pass(P) <> ?login(U,P)
... 2. Current state
8. <> !quit() 3. Current trace
9. !quit() <>
4. Inhibited traces
{}
?login(usr,
!user(usr)
psw)
!pass(psw)
...
...
!quit()
!quit()

15. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
... 1. Adaptation contract
8. <> !quit()
9. !quit() <> 2. Current state
3. Inhibited traces
{}
?login(usr,
!user(usr)
psw)
!pass(psw)
...
...
!quit()
!quit()

16. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
... 1. Adaptation contract
8. <> !quit()
9. !quit() <> 2. Current state
3. Inhibited traces
{}
?login(usr,
!user(usr)
psw)
!pass(psw)
...
...
!quit()
!quit()

17. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
... 1. Adaptation contract
8. <> !quit()
9. !quit() <> 2. Current state
3. Current trace
4. Inhibited traces
{} ?login(usr,
!user(usr)
psw)
!pass(psw)
...
...
!quit()
!quit()

18. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
... 1. Adaptation contract
8. <> !quit()
9. !quit() <> 2. Current state
3. Current trace
4. Inhibited traces
{} ?login(usr,
!user(usr)
psw)
!pass(psw)
...
...
!quit()
!quit()

19. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
... 1. Adaptation contract
8. <> !quit()
9. !quit() <> 2. Current state
3. Inhibited traces
{}
?login(usr,
!user(usr)
psw)
!pass(psw)
...
...
!quit()
!quit()

20. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
1. Adaptation contract
... 2. Current state
8. <> !quit()
9. !quit() <> 3. Current trace
4. Inhibited traces
{}
?login(usr,
!user(usr)
psw)
!pass(psw)
...
...
!quit()
!quit()

21. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
1. Adaptation contract
... 2. Current state
8. <> !quit()
9. !quit() <> 3. Current trace
4. Inhibited traces
{a?user(U):b?quit()}
?login(usr,
psw)
...
!quit()

22. Learning adaptor
Services Learning Adaptor Services
1. !user(U) <>
2. !pass(P) <> ?login(U,P)
1. Adaptation contract
... 2. Current state
8. <> !quit()
9. !quit() <> 3. Current trace
4. Inhibited traces
{a?user(U):b?quit(),
b?quit()} ?login(usr,
psw)
...
!quit()

23. Properties
Learning Adaptor
• Convergence
• Correctness
• Well-definedness
• Complexity (general case)
o O(SAd)
• Complexity (particular case)
o Time: O(max(A,d))
o Space: O(SA+Ad)
S - Number of contract states
A - Number of contract vectors
d - Maximum length of a trace

24. Evaluation

25. Evaluation
data

26. Evaluation
data

27. Evaluation
Sample standard deviation with 10 samples

28. Evaluation
55 inhibited traces vs 7123 transitions allowing
a total of 5466 different successful sessions
Sample standard deviation with 10 samples

29. Sporadic errors
• Communications are unstable due to hardware
failures (e.g., low battery) or environmental
interferences (e.g., shadow fading)
• We model this situation with a transition error rate (TER)
which is the probability of a certain synchronisation to
forcibly fail due to these sporadic errors
• The learning adaptors presented so far inhibit every
failed trace, therefore they converge to empty adaptors
when sporadic errors occur...
• ... therefore we have to forget those inhibited traces
that we cannot trust

30. Learning to forget
• Bounded learning: learn up to X traces and, when the
threshold is met, then forget the oldest/longest/least-
used/...
• Reset on empty adaptors: if we inhibit the empty trace,
then the adaptor is empty
o Then we can forget some (or all) traces in I
• Recognise sporadic errors: traces are inhibited
transition-by-transition. If some transitions are skipped,
it may be caused by a sporadic error

31. Learning to forget
• Bounded learning: learn up to X traces and, when the
threshold is met, then forget the oldest/longest/least-
used/...
• Reset on empty adaptors: if we inhibit the empty trace,
then the adaptor is empty
o Then we can forget some (or all) traces in I
• Recognise sporadic errors: traces are inhibited
transition-by-transition. If some transitions are skipped,
it may be caused by a sporadic error

32. Learning to forget
• Bounded learning: learn up to X traces and, when the
threshold is met, then forget the oldest/longest/least-
used/...
• Reset on empty adaptors: if we inhibit the empty trace,
then the adaptor is empty
o Then we can forget some (or all) traces in I
• Recognise sporadic errors: traces are inhibited
transition-by-transition. If some transitions are skipped,
it may be caused by a sporadic error

33. Learning to forget
• Bounded learning: learn up to X traces and, when the
threshold is met, then forget the oldest/longest/least-
used/...
• Reset on empty adaptors: if we inhibit the empty trace,
then the adaptor is empty
o Then we can forget some (or all) traces in I
• Recognise sporadic errors: traces are inhibited
transition-by-transition. If some transitions are skipped,
it may be caused by a sporadic error

34. Learning to forget
• Bounded learning: learn up to X traces and, when the
threshold is met, then forget the oldest/longest/least-
used/...
• Reset on empty adaptors: if we inhibit the empty trace,
then the adaptor is empty
o Then we can forget some (or all) traces in I
• Recognise sporadic errors: traces are inhibited
transition-by-transition. If some transitions are skipped,
it may be caused by a sporadic error

35. Learning to forget
• Bounded learning: learn up to X traces and, when the
threshold is met, then forget the oldest/longest/least-
used/...
• Reset on empty adaptors: if we inhibit the empty trace,
then the adaptor is empty
o Then we can forget some (or all) traces in I
• Recognise sporadic errors: traces are inhibited
transition-by-transition. If some transitions are skipped,
it may be caused by a sporadic error

36. Learning to forget
• Bounded learning: learn up to X traces and, when the
threshold is met, then forget the oldest/longest/least-
used/...
• Reset on empty adaptors: if we inhibit the empty trace,
then the adaptor is empty
o Then we can forget some (or all) traces in I
• Recognise sporadic errors: traces are inhibited
transition-by-transition. If some transitions are skipped,
it may be caused by a sporadic error

37. Evaluation with sporadic errors
TER = 0 in (0, 4000]; 10E−4 in (4000, 6000]; 10E−3 in (6000, 10000]; 0.01 in (10000, 12000];
0.1 in (12000, 14000]; 10E−3 in (14000, 16000]; 10E−4 in (16000, 18000]; and 0 in (18000, 20000]

38. Evaluation: close-up on athr
TER = 0 in (0, 4000]; 10E−4 in (4000, 6000]; 10E−3 in (6000, 10000]; 0.01 in (10000, 12000];
0.1 in (12000, 14000]; 10E−3 in (14000, 16000]; 10E−4 in (16000, 18000]; and 0 in (18000, 20000]

39. Zero-knowledge adaptation
Zero-knowledge
Adaptation Contract
{}

40. Zero-knowledge adaptation
Zero-knowledge
Adaptation Contract
1. !a <>
2. <> ?a
!a

41. Zero-knowledge adaptation
Zero-knowledge
Adaptation Contract
1. !a <>
2. <> ?a !a

42. Zero-knowledge adaptation
Zero-knowledge
Adaptation Contract
1. !a <>
2. <> ?a
3. <> !b
4. ?b <>
5. !c <>
...

43. Conclusions
• Contribution
o Learning adaptors are not synthesised
o They do not need to know the behaviour/protocol of the
services, they learn it
o Failed interactions are learnt and avoided
o The learning process is completely formalised and it
converges to the same solutions as traditional adaptors
o Simulation implemented in the ITACA toolbox*
• Future work
o Evaluate the feasibility of zero-knowledge adaptation
o Explore other techniques for contract generation
o Deploy and test on a real network
* http://itaca.gisum.uma.es/

44. Thank you!