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

1. 16th International World Wide Web ConferenceSpeeding up Adaptation of Web Service Compositions Using Expiration Times John Harney, Prashant Doshi LSDIS Lab, Dept. of Computer Science, University of Georgia

2. Web Process Adaptation • Most Web service compositions assume static environments • Service providers’ QoS parameters remain constant • Many environments are dynamic • Examples: • Supply Chain service provider’s rate of order satisfaction may decrease • Cost of using a service increases due to increase in price • Service response time increases due to network difficulties

3. Optimal Web Service Composition • “Optimality” – minimization of cost of using all services to complete process • Underlying objective – maintain Web process optimality • Depends on how accurately the QoS parameters are captured • Requires knowing any changes in QoS that may have occurred

4. Motivating Scenario – Supply Chain Manufacturer needs to order parts from a series of suppliers 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

5. Web Process Composition Using MDPs • We use Markov Decision Processes (MDP) to model our Web process (JWSR 05) • Components (states, actions, transition, cost) represent different aspects of the process • We solve the MDP to get the optimal policy • Policy determines the optimal decision to make at a particular state of the process n : S  A

6. Overview of Previous Approach • VOC – Value of Changed Information (ICSOC ’06) • 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 Value of Information

7. Overview of Previous 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

8. Overview of Previous Approach • We find and use the maximum VOC value VOC*(s) = max a€A VOC(s) Adaptation Procedure: VOC* < Cost of Querying Keep current policy Provider 1 VOC Candidate Processes Provider 2 VOC* … Query Provider Re-solve policy if needed VOC* > Cost of Querying Provider n VOC

9. Expiration Times • VOC*: Must compute VOC for EVERY Web service at every state of the process • Focus on services whose QoS parameters may change • Many services give guarantees of QoS parameters • Period of guarantee is the expiration time

10. VOCε - VOC with Expiration Times • VOCε allows us to find the max VOC using a smaller set of services • Theorem Given identical policies and start states, adaptation using VOCε and VOC* generate identical Web processes

11. Specification of Expiration Times • Expiration times could be specified using a WS-Agreement Expires on 27 Jan 2007 GPU Provider guarantees “40% availability”

12. VOCε - VOC with Expiration Times • How do we know which services have not expired? • Maintain a time counter ta for each service during composition and execution of the process • Increment the time counters of the services as the composition algorithm progresses • If ta > expiration time, the service is added to VOCε set Wait for provider’s response Query for information Find VOCε ta← ta + tVOCε ta← ta + tQlag ta← ta + tresponse

13. VOCε - VOC with Expiration Times • Query Lag Time – time needed for the process to receive QoS information from service provider

14. VOCε - VOC with Expiration Times • Response Time - time needed for the process to receive service provider response

15. Adaptive Web Process Composition Using VOCε Revised Adaptation procedure for VOC First eliminate non-expired services Then proceed in the same manner VOCε < Cost of Querying Keep current policy Provider 1 VOC Candidate Processes Provider 2 VOCε … Query Provider Re-solve policy if needed VOCε < Cost of Querying Provider n VOC

16. VOCε - VOC with Expiration Times • In the worst case, the complexity of VOCε is the same as VOC* • Occurs when all services have expired • In the best case, VOCε is not needed • Occurs when no services have expired • In the average case, VOCε performs in between • Shown empirically

17. Empirical Results • Measured the average process cost over different costs of querying • Query random strategy – randomly pick a service to query • Do nothing strategy – do no adaptation • VOCε strategy Patient Transfer Clinical Pathway Supply Chain Scenario

18. Empirical Results • Computational Time Savings • Measured the average process execution time over a range of expiration times • VOC* is the upper bound - VOCε does no worse • Do nothing is the lower bound • VOCεprocess execution times occur between the two bounds Patient Transfer Clinical Pathway Supply Chain Scenario

19. Discussion • Intelligent adaptation mechanisms (eg VOC) are expensive • VOC computational overhead • Compute VOC for all services • VOCε approach • Uses expiration times to reduce the set of candidate services • Future work • Address VOC computational overhead

20. Thank you Questions