Trustworthy Service Selection and Composition

CHUNG-WEI HANG

MUNINDAR P. Singh

A. Moini

Content

Service-oriented computing

Preview (paper’s key idea)

Probabilistic service selection &

composition approaches

Experimental results

Summary

Service-Oriented ComputingEvery computing resource is packaged as a

service

Services are application building blocks ◦ unit of functionality◦ unit of integration◦ unit of composition

Individual services can be “composed” to create more “composite” services

Service have dependencies on other constituent services

Consumer service does not have any knowledge of dependencies of services it consumes

Service-Oriented ComputingChallenges

Services composition (binding) is a design time activity◦ based on functional properties meeting consumer

requirements, not quality attributes functional properties: service types, published as WSDL

contract quality attributes: throughput, response time, availability..

Service quality varies by service instance and over time ◦ service quality may change unpredictably

ContributionsProbabilistic trusted-aware service

selection and composition model◦ takes into account service consumer’s requirements (e.g.

qualities of service)

◦ takes into account service composition patterns

◦ considers service qualities as they apply to service instances quality component service may affect the whole composition

Example: reliability

◦ rewards & punishes constituent services dynamically

Service Composition Patterns(BEPL Primitives)

SWITCH• chooses exactly one component based on some criteria

MAX• composes quality by inheriting from child with highest quality

valueMIN

• composes quality by inheriting from child with lowest quality• throughput for sequence

SUM • yields composite quality value as sum of quality values

obtained from all constituent servicesPRODUCT

• yields composite quality value as product of quality values obtained from all constituent services.

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BEPL Services Diagram

Trust-Aware Service Selection Model

Trust-Aware Service Selection ModelTrustworthiness of a service is estimated based on

direct experience previous QoS received from service

Consumer maintains its own local model to determine if to reward or penalize services based on direct experience◦ selects services and composes them into a composite

service◦ evaluates composite service with respect to service

quality attributes◦ applies a learning method to update its model for the

services Special case: when selecting atomic service, consumer has

less information to learn from

Trust-Aware Service Selection ModelsTwo Alternatives

Bayesian Model◦ models compositions via Bayesian networks in partially

observable settings◦ captures dependency among composite and constituent

services◦ adaptively updates trust to reflect the most recent quality◦ uses online learning to track service behavior and shows how

composite service’s quality depends upon its constituents’ quality

Beta mixture Model◦ can learn not only distribution of composite quality, but also

responsibility of a constituent service in composite quality without actually observing the constituent’s performance.

◦ learns quality distribution of the services◦ provides how much each constituent service contributes to

overall composition

Trust-Aware Service Selection ModelsTwo Alternatives

Must be able to construct model from incomplete observations

Not all service qualities are observable from the consumers’ point of view

service quality attribute are represented as real numbers in interval [0, 1]:

represent observation of a particular quality of service instance d at time t

,…, )

Service Composition Bayesian Model

P(T) Probability of obtaining satisfactory quality from service T

Trust

CompositeService

atomic

Service Composition Bayesian Model

Conditional probability table associated with each node provides a basis for determining how much responsibility to assign to constituent services

Conditional probabilities represent level of trust consumer places in constituent services in composition

Service CompositionDealing with Incomplete Data

model variables may not be observable data is often incomplete Variables w/o data considered latent variable

Expectation Maximization (EM) is used to optimally estimate distribution parameters which are then used to calculate the expected values of latent variables

Service CompositionDealing with Incomplete Data

Example: Travel service depends hotel service Consumer observes that has reliability 1 at time-step t but

does not observe the reliability of at time So, expected reliability of , can be used as nominal

observation, i.e. , Completed data,

can be used as the observation in M step to update the parameter estimates using Bayesian inference.

New parameter estimate can be calculated by the posterior mean of

The E and M steps are executed iteratively until the estimation converges.

Service CompositionBeta-Mixture Model

Superposition of multiple Beta probability density components, representing multiple subpopulations

Each mixing coefficient is an indicator of corresponding component’s responsibility, i.e., how much contribution component makes toward composite quality

Mixture dist. is governed by two parameters:

Service CompositionBeta-Mixture Model

Mixture distribution estimated by maximizing log-likelihood function using EM algorithm

: binary latent variable, indicating whether an observationis from component k. Exactly one of the equals 1; rest are zero.

𝑍 𝑘

Service CompositionBeta-Mixture Model Estimation

EM Algorithm Steps

EM is a sequential online learning algorithm: it is repeated whenever the consumer makes new observations.

Experimental Evaluations

Composition Operator Service Quality Metrics and Interaction

Types

SWITCH• chooses exactly one of its children based on a predefined

multinomial distribution• simulates composite quality based on one children

MAX• composes quality by inheriting from child with highest

quality value• relates to latency for flow.

SWITCH• chooses exactly one children based on predefined multinomial

distribution• simulates composite quality based on one children

MAX• composes quality by inheriting from child w/ highest quality

value• represents latency for flow

MIN • composes quality by inheriting from child with lowest quality• throughput for sequence

SUM • yields composite quality value as sum of quality values obtained

from all children• relates to throughput for flow

PRODUCT• yields composite quality value as product of quality values

obtained from all children.• relates to failure for flow

Experimental ResultsBayesian Appraoch

Composite Service C Trust EstimationSWITCH Operator

Composite Service C Conditional Trust (SWITCH Operator)

Good Service Bad Service

Bayesian vs. NaïvePrediction Errors

(80% missing data)

Conditional Trust in Composite Service

MAX

MIN

40% data missing

Dealing with Dynamic Behavior

Random Walk Service

Cheating Constituent Service

Actual and Estimated Parameters

Estimated Beta-mixture & Actual Distribution and samples of trust

(SWITCH composition)

Beta-mixture learns accurate distributions of both component services.

One provides good service (left peak); the other provides bad service (right peak).

Kolmogorov-Smirnov Test FCM-MM vs. Beta-mixture

Prediction Error Nepal et al. vs. Beta-mixture

Powerful means of estimating quality distribution of a composite service w/o knowing quality of constituents

Accurately estimates responsibilities of each constituent service

Limitations Difficult to learn component distributions when

composite distribution is unimodal. Accuracy may be improved if constituent services qualities are partially observable.

Difficult to learn constituent services that rarely contribute due to lack of evidence; beta-mixture can correctly identify those services.

Cannot track dynamic behavior.

Beta Mixture Model

Limitationslack of unconditional trust in the constituent

services

assumption of a least partial observability

Bayesian Model

Key featuresTwo probabilistic models for trust-aware service

selection and composition can handle variety of service composition patterns

Can capture relationships between qualities of service offered by composite service and qualities offered by its constituents

Trust is learned sequentially from directed observations then, combined with indirect evidence in terms of service qualities

Can handle incomplete observations

Summary

Key featuresEach consumer must monitor quality attributes

of services it interacts with & maintain own model local knowledge

Model evaluation technique: simulation

Future research ideaApply Structural EM, instead of parameter estimation, to learn not only trust information but also service dependency graph structure: learned structure can be used as a basis for suggesting new service compositions

Summary