Toward Optimal and Efficient Self-Adaptation in Large Web Processes

1. Toward Optimal and Efficient Self-Adaptation in Large Web Processes Prashant Doshi Assistant Professor 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 • E.g.. delay in satisfying order adverse drug reaction • New knowledge • E.g.. New drug alert Component Volatility • Change in the state of the process participants • E.g.. Web service failure or abnormal behavior • Expected Volatility • Events known to occur with some chance • E.g.. delay in satisfying order Worsening of patient symptoms Unexpected Volatility • E.g.. 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: E.g. 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. Centralized Adaptation Decentralized Adaptation Hybrid approaches DecreasingOptimality 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 Computational Efficiency

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 controls the 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 • E.g. 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 order satisfaction may change arbitrarily • Cost of service may change arbitrarily • Research Challenges • How to be cognizant of the change • 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 expected to be: • 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 1. SM calculates VOC for each service provider involved in Web process Prov 1 Prov 2 Prov n … VOC * VOC 2. PM finds provider whose changed parameter induces the greatest change in process (VOC*) VOC VOC* < Cost of Querying VOC* > Cost of Querying 3. Compare VOC* to cost of querying Keep current process Query Provider Re-compute process if needed

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 (Harney&Doshi, WWW 07) • Other approaches needed

21. Future Work • More study and characterization of volatility • Research on hybrid approaches • Handle component volatility • Candidate approaches: A-WSCE architecture (Chafle et al.06) • k-service redundancy and k-process redundancy

22. Thank YouQuestions