[PPT] - Towa To r dsSe Se l f - Di Di a a g gnos i ng ng W e PowerPoint Presentation

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Se l f M a n 05

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To Towa r dsSe Se l f

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Di a g a gnos i ng ng W e W e b Se Se r v i c e c e s

L. Ardissono • L. Console • A. Goy • G. Petrone
C. Picardi • M.Segnan • D. Theseider Dupré

Dipartimento di Informatica Unversità di Torino (Italy)

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M o M ot i v a v a t i

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Current practice for dealing with faults in distributed

software systems:

exception handling
no attempt at identifying causes
Aim: Advanced diagnostic capabilities for complex Web

services (composed from individual services)

identifying the faulty service to apply the proper recovery

action

Towards self-healing Web Servioces
We propose a Model-based diagnosis approach for localizing

the faulty service

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M o M ot i v a v a t i ng ng e x e x a mpl e

If the customer receives the wrong book, which are the possible causes?

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M o M ode l

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Ba s e d Di Di a g a gnos i s s ( 1 ( 1 )

An approach to automated diagnosis
from AI (Artificial Intelligence) and Engineering
Diagnosis:
finding the cause(s) of an unexpected behavior
determining the most appropriate repair/recovery action
Detection VS Identification VS Recovery (Repair)
Main application
artefacts
Basic assumption
a Model of the artefact is available

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M o M ode l

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Ba s e d Di Di a g a gnos i s s ( 2 ( 2)

Different approaches to modelling.
We focus on component-oriented modelling:
Structure of the artefact (the Complex Service):
components (services) and their connections to define

super-components (component hierarchy)

Function or Behaviour of its component types

(individual or elementary) services:

Nominal behavior
Behavior in presence of faults
Qualitative Models
Variables express qualitative properties of the system
e.g: low/high or present/absent or correct/incorrect

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M o M ode l

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Ba s e d Di Di a g a gnos i s s ( 3 ) ( 3 )

Design Textbooks First principles …

System Model Actual System

Predicted behaviour Observed behaviour

DIAGNOSIS

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Ex Ex a mpl e

Pump Pipe Actuator In C E Out

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Co Compone nt

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i e n e nt e d e d M o M ode l so sofW Ss( 1 )

Model of WS: abstraction of its computation
A set of activities with I/O variables
activity ≡ component (smallest diagnosable unit) with

behaviour modes ok and fail

Model: Relation between such variables
Which variables are affected by each activity
Which variables may result as abnormal (ab) in case an

activity fails

Assumption:
for each activity in the ok mode, all inputs ok ⇒ all
utputs ok

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Co Compone nt

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i e n e nt e d e d M o M ode l so sofW Ss( 2)

Diagnosis is activated by alarms in the

WS

An alarm a
typically corresponds to a mismatch of two

variables x and y

Or to an unexpected value of a variable
The model contains also checkpoints:
analogous to alarms
evaluated on demand, not automatically.

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De De c e nt r a r a l i z e z e d Di Di a g a gnos i s

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De De c e nt r a r a l i z e z e d Di Di a g a gnos i s

Global Diagnoser

no initial info

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De De c e nt r a r a l i z e z e d Di Di a g a gnos i s

Global Diagnoser

no initial info

Local Diagnoser

local model + alarms + checkpoints

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De De c e nt r a r a l i z e z e d Di Di a g a gnos i s

Global Diagnoser

no initial info

Local Diagnoser

local model + alarms + checkpoints

Web Service

sends messages to local diagnoser

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Ou Ou r Appr ppr

a

c h

We provide:
a specification of local diagnoser operations
a formal characterization of local diagnoser operations
A communication protocol between local and global

diagnosers

an algorithm for the Global Diagnoser
starts with no information on local services
the algorithm only assumes that local diagnosers meet the

specifications ofr their operations

the algorithm merges information from local diagnosers

and decides which local diagnosers to contact.

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St St a r a r t i ng ng Di Di a g a gnos i s s Up Upon Al Al a r a r ms

Something’s wrong

corresponding local diagnoser reacts to a fault message.

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St St a r a r t i ng ng Di Di a g a gnos i s s Up Upon Al Al a r a r ms

Initial info:
local observations (alarms + checkpoints) OBS
Compute:
a set of candidate diagnoses hypotheses of

misbehaviour that explain OBS

internal misbehaviour: errors occurred inside the WS
external misbehaviour: errors in inputs received from
ther WSs (blame on other services)
consequences of each hypothesis on service outputs
can be used to validate/discard a candidate diagnosis
Standard MBD techniques can be applied.

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Lo Loc a lCa ndi da da t eDi Di a g a gnos i s

A local candidate diagnosis contains three elements: hypotheses on local behaviour blames on other (input) services consequences of hypotheses

n other (output) services

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COLLECT

local candidate diagnoses

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QUESTION

ask for blame explanation

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VALIDATE

ask for consequence validation

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Lo Loc a lDi a g a gnos e r s-Ex Ex pl a n a na t a t i

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Local diagnoser receives blames
It produces local candidate diagnoses that explain
bservations AND blames.
additional hypotheses of internal misbehaviour
additional blames
additional consequences
New local candidate diagnoses:
merged with the ones that originated the blame by the

global diagnoser

If no explanation:
the candidate diagnosis that originated the blame is

rejected by the global diagnoser

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Lo Loc a lDi a g a gnos e r s-Va Va l i da da t i

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Local diagnoser receives consequences
It verifies through local observations whether the

consequences hold.

Produces:
additional consequences on other services
If initial consequences hold:
the global diagnoser adds new consequences to the

local candidate diagnosis that originated them.

If initial consequences do not hold:
the candidate diagnosis that originated them blame is

rejected by the global diagnoser.

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Cha r a r a c a c t e r e r i z a z a t i

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Lo Loc a lDi a g a gnos e r s

Candidate diagnoses are represented by partial

assignments to model variables

assignments of behaviour modes to internal activities
assignments of correctness status to model variables
For both explanation/validation:
local diagnosers receive the parts of the assignments

that concerns them

work by completing partial assignments
operation can be carried out by standard MBD techniques
Both can be characterized in the same way
one operation that explains and validates at the

same time.

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Th TheGl

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Each request for explain/validate
produces new blames
produces new consequences
The Global Diagnoser:
repeatedly asks for explanations and validations
until there is nothing to explain/validate
A local diagnoser may be invoked multiple times
the general case does not assume a persistency of

local diagnosers

each invocation can be considered separately
however persistency improves efficiency.

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An An I nt nt e l e l l i ge ge nt ntGl Gl

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The global diagnoser keeps track of candidate

diagnoses

information from different local diagnoser mantained as a

set of partial assignments

Intelligent behaviour to reduce overhead:
depending on assigned/unassigned variables may avoid

questioning some services

May exploit (if present) information on workflow
in order to focus diagnosis
in order to select an optimal questioning order, to

avoid multiple calls to the same local diagnoser

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Co Conc l u s u s i

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nd Fu Fu t u r e u r e W o W or k

Advantages of the approach:
reduction of communication overhead
decentralized VS purely distributed
does not explore the whole model if not necessary
no restrictive assumptions on the models
abstract models of correctness propagation
could be at least partially derived automatically (to

investigate)

Future work:
exploit coordination mechanisms and coordination info
local diagnosers only characterized
propose efficient algorithms for local diagnosers