Trustworthy Service Selection and Composition CHUNG-WEI HANG MUNINDAR P. Singh A. Moini.
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Transcript of Trustworthy Service Selection and Composition CHUNG-WEI HANG MUNINDAR P. Singh A. Moini.
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.
http://www.deltalounge.net/wpress/tag/soa-suite/page/2/
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