Trustworthy Service Selection and Composition

1. Trustworthy Service Selection and Composition CHUNG-WEI HANG MUNINDAR P. Singh A. Moini

2. Content • Service-oriented computing • Preview (paper’s key idea) • Probabilistic service selection & composition approaches • Experimental results • Summary

3. Service-Oriented Computing • Every 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

4. Service-Oriented Computing Challenges • 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

5. Contributions • Probabilistic 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

6. Service Composition Patterns (BEPL Primitives) • SWITCH • chooses exactly one component based on some criteria • MAX • composes quality by inheriting from child with highest quality value • 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 constituent services • PRODUCT • yields composite quality value as product of quality values obtained from all constituent services.

7. BEPL Services Diagram http://www.deltalounge.net/wpress/tag/soa-suite/page/2/

8. Trust-Aware Service Selection Model

9. Trust-Aware Service Selection Model • Trustworthiness 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

10. Trust-Aware Service Selection Models Two Alternatives • Bayesian Model • models compositions via Bayesian networks in partially observable settings • capturesdependency 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 • provideshow much each constituent service contributes to overall composition

11. Trust-Aware Service Selection Models Two 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 • ,…, )

12. Service Composition Bayesian Model atomic Trust Composite Service P(T) Probability of obtaining satisfactory quality from service T

13. 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

14. Service Composition Dealing 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

15. Service Composition Dealing 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.

16. Service Composition Beta-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:

17. Service Composition Beta-Mixture Model • Mixture distribution estimated by maximizing log-likelihood function using EM algorithm : binary latent variable, indicating whether an observation is from component k. Exactly one of the equals 1; rest are zero.

18. Service Composition Beta-Mixture Model Estimation EM Algorithm Steps EM is a sequential online learning algorithm: it is repeated whenever the consumer makes new observations.

19. Experimental Evaluations

20. 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.

21. 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

22. Experimental ResultsBayesian Appraoch

23. Composite Service C Trust Estimation SWITCH Operator

24. Composite Service C Conditional Trust (SWITCH Operator) Good Service Bad Service

25. Bayesian vs. Naïve Prediction Errors (80% missing data)

26. Conditional Trust in Composite Service MAX MIN 40% data missing

27. Dealing with Dynamic Behavior

28. Random Walk Service Cheating Constituent Service

29. Actual and Estimated Parameters

30. 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).

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

32. Prediction Error Nepal et al. vs. Beta-mixture

33. Beta Mixture Model • 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.

34. Bayesian Model • Limitations • lack of unconditional trust in the constituent services • assumption of a least partial observability

35. Summary • Key features • Two 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

36. Summary • Key features • Each consumer must monitor quality attributes of services it interacts with & maintain own model local knowledge • Model evaluation technique: simulation • Future research idea • Apply Structural EM, instead of parameter estimation, to learnnot only trust information but also service dependency graph structure: learned structure can be used as a basis for suggesting new service compositions