1. Toward Optimal and Efficient Adaptation in Web Processes Prashant Doshi LSDIS Lab., Dept. of Computer Science, University of Georgia Joint work with: Kunal Verma, Yunzhou Wu, and Amit Sheth

2. Outline of the Talk Understanding Volatility –Two characterizations Our Approach –Abstract Processes and Service Managers –Adaptation as a Decision-Making Problem A Framework for Studying Adaptation –Evaluation criteria Optimality Computational Efficiency Some Experimental Results Value of Changed Information –Definition –Experimental Results Discussion and Future Work

3. Understanding Volatility Data Volatility –Atypical input and execution data Eg. delay in satisfying order adverse drug reaction –New knowledge Eg. New drug alert Component Volatility –Change in the state of the process participants Eg. Web service failure or abnormal behavior Expected Volatility –Events known to occur with some chance Eg. delay in satisfying order Worsening of patient symptoms Unexpected Volatility –Eg. New drug alert New co-morbidity data volatility component volatility expected (with some chance) unexpected

4. Abstract Processes and Service Managers Pre-specified abstract processes –Ordering of activities –Inter-activity constraints: Eg. Coordination constraints Process and Service Managers Heart Failure Clinical Pathway

5. Abstract Processes and Service Managers Our architecture –Two tiers Resources Layer Control Layer

6. A Framework for Studying Adaptation Two criteria for evaluating approaches –Cost-based optimality –Computational efficiency Formalize adaptation as a decision problem –Two general choices Ignore the change React to the change –Example methodology: Markov decision processes (MDP) Decreasing Optimality Decreasing Computational Efficiency Centralized Adaptation Decentralized Adaptation Hybrid approaches

7. A Framework for Studying Adaptation Centralized Approaches –PM is responsible for adaptation Global oversight Decentralized Approaches –SMs are responsible for local adaptation Local oversight Difficult to manage inter-activity constraints Hybrid Approaches –Both PM and SMs share the responsibility of adaptation Global and local oversight

8. Establishing the Ends of the Spectrum Centralized adaptation to expected data volatility Example: M-MDP method (Verma, Doshi et al. ICWS 06) Properties: Theorem: M-MDP adapts the process optimally to exogenous events expected with some chance and with coordination constraints PM has global oversight and controlsthe SMs Does not scale well: Complexity exponential in the number of SMs Computer assembly

9. Establishing the Ends of the Spectrum Decentralized adaptation to expected data volatility Example: MDP-CoM method (Verma, Doshi et al. ICWS 06) Challenge: Satisfying coordination constraints Properties: Scalable to multiple SMs Not optimal Computer assembly Coordination Mechanism

10. Research Challenge: Hybrid Approaches Idea #1: Least-commitment –PM steps in only when needed Eg. when deciding on a coordinating action Idea #2: Inter-SM communication –Motivation for communication: Regret

11. Some Experimental Results Adapting to delay in supply chain Choices Wait out the delay Change the supplier M-MDP incurs the least average cost MDP-CoM the most Runtime for MDP-CoM remains fixed as number of activities increases Decentralized adaptation is parallelizable

12. Related work Verification of correctness of manual changes to control flow –Adept (Reichert&Dadam98), Workflow inheritance (Aalst&Basten02), inter-task dependencies (Attie et al.93) Event Condition Action (ECA) rules for adaptation –Agentwork (Muller et al.04) Change of service providers based on migration rules in E-Flow (Casati et al.00) We complement previous work in this area by emphasizing: –Cost based optimality –Computational efficiency

13. Unexpected Data Volatility Example –Rate of supplier satisfaction may change arbitrarily –Cost of service may change arbitrarily Research Challenges 1.How to be cognizant of the change 2.When to adapt to the change Our approach –Query the service providers for revised information Cost of querying! –Adapt when information is useful

14. Possible Approaches Query a random provider for relevant information –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 Value of Changed Information (VOC) (Harney&Doshi,ICSOC06) –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)

15. Value of Changed Information VOC –Measures how “ badly ” the current process is expected to perform 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 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

16. Manufacturer’s Beliefs For Supply Chain Example - Beliefs of Order Satisfaction

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

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

19. Discussion Understanding dynamic environments is crucial –Categorizations needed Data and component volatility Expected (with probabilities known a’priori) and unexpected events Other taxonomies? A framework for studying adaptation –Criteria for evaluation Cost-based optimality Computational efficiency –We established the ends of the spectrum Centralized (M-MDP) and decentralized approaches (MDP-CoM) Research on hybrid approaches needed

20. Discussion Value of changed information (VOC) –Unexpected and arbitrary data volatility –Query for revised information Obtains revised information expected to be useful Avoids unnecessary queries VOC calculations are computationally expensive –Knowledge of service parameter guarantees may be used to eliminate unnecessary VOC calculations (WWW07 submission) –Other approaches needed

21. Future Work Handle component volatility –Candidate approaches: A-WSCE architecture (Chafle et al.06) –k-service redundancy and k-process redundancy Integrate VOC into A-WSCE architecture –Collaboration with B. Srivastava

22. Thank You Questions