John Harney, Prashant Doshi LSDIS Lab, Dept. of Computer Science, University of Georgia

1. 4th International Conference on Service Oriented ComputingAdaptive Web Processes Using Value of Changed Information John Harney, Prashant Doshi LSDIS Lab, Dept. of Computer Science, University of Georgia

2. Web Process Composition Supply Chain Process Preferred Supplier Service Rate of Order Satisfaction Spot Market Service Rate of Order Satisfaction Start Finish Invoke Response Invoke Response Inventory Service Rate of Order Satisfaction Other Supplier Service Rate of Order Satisfaction Traditional Web process compositions assume static environments

3. Web Process Composition Many environments are dynamic Preferred Supplier Service Rate of Order Satisfaction Spot Market Service Rate of Order Satisfaction Start Finish Invoke Response Invoke Response Inventory Service Rate of Order Satisfaction  Other Supplier Service Rate of Order Satisfaction Preferred Supplier may be better choice Supply Chain Process Inventory satisfaction rate decreases

4. Optimal Web Process Composition • Underlying objective • Web process optimality • Depends on how accurately the environment is captured • Requires finding any changes that may have occurred

5. Motivating Scenario – Supply Chain

6. Motivating Scenario – Supply Chain • How does process environment change? • Example: Supply Chain (Inventory service) • Rate of satisfaction of a supplier service • Eg Inventory satisfaction rate decreases or increases • Cost of using a service • Cost of the Inventory service decreases or increases • Other parameters (response time, QoS, etc)

7. Possible Adaptation Approaches • Do Nothing (Ignore the changes) • Advantages • Simple • No additional cost or computational overhead of adaptation • Disadvantages • Sub-optimal Web process • Web process can do better

8. Possible Adaptation Solutions • Query a random provider for relevant information (eg. Inventory) • Advantages • Up-to-date knowledge of queried service provider • Performs no worse than “do nothing” strategy • Disadvantages • Querying for information not free • Paying for information that may not be useful • Information may not change Web process

9. Overview of Our Approach • VOC – Value of Changed Information • Decides if obtaining information is: • Useful • Will it induce a change in optimality of Web process? • Cost-efficient • Is the information worth the cost of obtaining it? • Extension of VOI (Value of Information)

10. Overview of Our Approach • VOC • Measures how “badly” the current process is performing in changed environment • Defined as the difference between: • Expected performance of the old process in the changed environment • Expected performance of the best process in the changed environment

11. Web Process Composition Using MDPs • Markov Decision Processes (MDP) (see JWSR 05) • Definition:M = (S, A, T, C) S : States, A: Actions, • Actions may be non-deterministic T: Transition function, • States are fully observable S x A (S) C: Cost function S x A Real • Perform stochastic optimization using Dynamic Programming • Value function heuristic : • Optimal Policy n : S  A • (Minimize expected cost)

12. Web Process Composition Using MDPs S: Feature-based state space using propositions • E.g. Mftg. Inventory Availability Yes|No|Unknown A: WS invocations • E.g. Check Mftg. Inventory Status Check Preferred Supplier Status T: An estimate of the “ground truth” probabilities • E.g. T(Mftg. Inventory Avail = Yes| Check Mfg. Inventory Status, Mftg. Inventory Availability= Unknown) = 0.33 C: Costs may be obtained through costing analysis Π*: Determines which service to invoke at a particular state

13. Formalizing VOC • Supply Chain Example • Querying Transition function T (satisfaction rate of suppliers in supply chain) • Changed Transition function – T’(.|a,s’) • Current Policy Value - Vπ(s|T’) • Best Policy Value - Vπ*(s|T’)

14. Formalizing VOC • Actual service parameters are not known • Must average over all possible revised parameters • We use a belief of revised values • Could be learned over time

15. Manufacturer’s Beliefs Example - Beliefs of Order Satisfaction

16. Adaptive Web Process Composition 1. Calculate VOC for each service provider involved in Web process Prov 1 Prov 2 Prov n … VOC * VOC 2. Find provider whose changed parameter induces the greatest change in policy (VOC*) VOC VOC* < Cost of Querying VOC* > Cost of Querying 3. Compare VOC* to cost of querying Keep current policy Query Provider Re-solve policy if needed

17. Our Services Oriented Architecture

18. Empirical Results • Simulated volatile Supply Chain & Patient Transfer scenarios for: • Do Nothing • keeping the same process • Query random provider • Obtaining information from one provider at each state • Reformulate composition => Resolve policy • VOC • VOC for determining if query is needed • Reformulate composition if need be

19. Empirical Results • Measured the average process cost over a range of query cost values • VOC queries selectively -- query random strategy cost grows at a larger rate than VOC • VOC performs no worse that the do nothing strategy Supply Chain Web Process Patient Transfer Web Process

20. Discussion • Web Process environments are dynamic • Processes must adapt to changes in environment to remain optimal • Obtaining the revised information is crucial but may be costly • VOC approach • Obtains revised information expected to be useful • Avoids unnecessary queries

21. Future Work • VOC calculations are computationally expensive • Knowledge of service parameter guarantees may be used to eliminate unnecessary VOC calculations

22. Thank you Questions