Learning from Failures: a Lightweight Approach to Run-Time Behavioural Adaptation

14 of September, 2011 Oslo, Norway

Learning from Failures:a Lightweight Approach to

Run-Time Behavioural Adaptation

J. Antonio Martín, Antonio Borgi and Ernesto PimentelUniversity of Málaga - University of Pisa

FACS'11

Motivation

In pervasive computing scenarios, we have thousands of interacting entities with limited resources which may move,

change and degrade over time

Motivation

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

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

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

Assumptions

• We do not know the actual behaviour of the serviceso New unforeseen services might come upo Service behaviour may change unexpectedly due to

hardware issueso This causes incompatibilities in signature and behaviour

• The nodes have limited resourceso Computation, memory and battery are scarceo Communications quickly deplete the batteryo Bounded services

• We have an adaptation contracto This is based on the signature of the serviceso And (optionally) a declarative representation of the goal of

the adapted sessiono Alternatively, we could use zero-knowledge adaptation

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


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


An adaptor is specified in a declarative manerby an adaptation contract

Traditionally, adaptors are synthesised knowing the contract and the behaviour of the services

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

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

Adaptation contract

1. !user(U) <>2. !pass(P) <> ?login(U,P) ...8. <> !quit()9. !quit() <>

Adaptation ContractServices Services

!user(usr)!pass(psw)

...!quit()

?login(usr, psw)

...!quit()

Learning adaptor

Learning Adaptor1. !user(U) <>2. !pass(P) <> ?login(U,P) ...8. <> !quit()9. !quit() <>

1. Adaptation contract2. Current state3. Current trace4. Inhibited traces

{}

Services


...!quit()

Services

?login(usr, psw)

...!quit()

Learning adaptor


1. Adaptation contract2. Current state3. Inhibited traces

{}

Services


...!quit()

Services

?login(usr, psw)

...!quit()

Learning adaptor



{}

Services


...!quit()

Services

?login(usr, psw)

...!quit()

Learning adaptor


1. Adaptation contract2. Current state3. Inhibited traces

{}

Services


...!quit()

Services

?login(usr, psw)

...!quit()

Learning adaptor



{}

Services


...!quit()

Services

?login(usr, psw)

...!quit()

Learning adaptor



{a?user(U):b?quit()}

Services Services

?login(usr, psw)

...!quit()

Learning adaptor



{a?user(U):b?quit(),b?quit()}

Services Services

?login(usr, psw)

...!quit()

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 statesA - Number of contract vectorsd - Maximum length of a trace

Evaluation

Evaluation

data

Evaluation

Sample standard deviation with 10 samples

Evaluation

Sample standard deviation with 10 samples

55 inhibited traces vs 7123 transitions allowing a total of 5466 different successful sessions

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

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 emptyo 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

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]

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]

Zero-knowledge adaptation

{}

Zero-knowledgeAdaptation Contract


1. !a <>2. <> ?a


!a


1. !a <>2. <> ?a3. <> !b4. ?b <>5. !c <> ...


Conclusions

• Contributiono Learning adaptors are not synthesisedo They do not need to know the behaviour/protocol of the

services, they learn ito Failed interactions are learnt and avoidedo The learning process is completely formalised and it

converges to the same solutions as traditional adaptorso Simulation implemented in the ITACA toolbox*

• Future worko Evaluate the feasibility of zero-knowledge adaptationo Explore other techniques for contract generationo Deploy and test on a real network

* http://itaca.gisum.uma.es/

Thank you!

Learning from Failures: a Lightweight Approach to Run-Time Behavioural Adaptation

Technology

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