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Haley: A Hierarchical Framework for Logical Composition of Web Services

IEEE International Conference on Web Services 2007. Haley: A Hierarchical Framework for Logical Composition of Web Services. Haibo Zhao , Prashant Doshi LSDIS Lab, Dept. of Computer Science, University of Georgia. Outline . Introduction Motivating scenario Background

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Haley: A Hierarchical Framework for Logical Composition of Web Services

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  1. IEEE International Conference on Web Services 2007 Haley: A Hierarchical Framework for Logical Composition of Web Services Haibo Zhao, Prashant Doshi LSDIS Lab, Dept. of Computer Science, University of Georgia

  2. Outline • Introduction • Motivating scenario • Background • Model: First order Semi-Markov decision processes (FO-SMDP) • Composing nested Web processes using Haley • Architecture • Experiment & Discussion

  3. Introduction • Web service composition • Business processes with Web services as components • Existing approaches to composition: AI planning • Classical planning techniques • Golog, Model checking-based planning, HTN planning, Synthy • Decision-theoretic planning • MDP (Doshi2004) • Limitations • Classical planning assumes deterministic behavior of Web services • Guarantee correctness but not optimality • State space explosion • Cannot operate directly on WS descriptions in FOL

  4. Our Approach: Haley • Stochastic SMDP model • Handle uncertainties in WS invocation • Provide cost-based optimality • Hierarchical model to represent the hierarchies in Web processes • Address the scalability problem • FOL based representation • Directly operate on WS description in FOL

  5. Handling Orders in Supply Chain Level 1: Composition using composite FO-SMDP Level 0: Composition using primitive FO-SMDP Abstract action Level 0: Composition using primitive FO-SMDP Abstract action

  6. Background: Probabilistic Situation Calculus[Reiter01] • A FOL based framework for representing actions, changes and reasoning about them • Probabilistic Situation calculus elementsActions: parameterirzed FO termsReceiveOrder(o)Situations: sequence of actions representing the state of the world do(ReceiveOrder(o),s0)Fluents: situation-dependent relations and functions whose truth values may vary HaveOrder(o, s)Nature’s Choices: capture stochastic results of actionsChoice(CheckCustomer(o), a) ≡ a = CheckCustomerS(o) ∨ a = CheckCustomerF(o)Probabalities for nature’s choices:Pr(CheckCustomerS(o),CheckCustomer(o), s) = 0.9Pr(CheckCustomerF(o),CheckCustomer(o), s) = 0.1Precondition Axioms:HaveOrder(o, s) ⇒ Poss(CheckCustomer(o), s)Successor state Axioms:Describe the effects on fluentsPoss(a, s) ⇒HaveOrder(o, do(a, s)) ⇔ a = ReceiveOrderS(o) ∨ (HaveOrder(a, s) ∧ a ≠CancelOrderS(o))

  7. First Order MDP(FO-MDP) [Boutilier 01] • Probabilistic situation calculus representation • allows concise specification of complex domains • Specify lump sum reward/cost and utilities with case notationkCase(A(x)) = case[ A(x) = CheckCustomer(o), 2; A(x)= VerifyPayment(o), 3; A(x) = ChargeMoney(o),2 ] • Avoid explicit state and action enumeration • A decision-theoretic regression algorithm for solving FO-MDPs

  8. First Order Semi-MDPs (FO-SMDP) • FO-SMDP is a temporal generalization of FO-MDPs: The sojourn time of actions are modeled with a density function; and the system will incur an action-duration cost at an accumulating rate • Case notation of sojourn time distribution • Case notation of accumulating rate • Total reward of a state-action pair • Representing total reward and utilities with case notation, FO-SMDP can be solved analogously to FO-MDP using DT regression

  9. Level 1: Composition using composite FO-SMDP Level 0: Composition using primitive FO-SMDP Abstract action Level 0: Composition using primitive FO-SMDP Abstract action

  10. Elicitation of Model Parameters (level 0) Level 0:Model parameters may be obtained from WSDL-S/SAWSDL, OWL-S descriptions of Web services, and service level agreements • Compile situation calculus axioms from preconditions and effectse.g.WS: ChargeMoney(o)Precondition: V alidCustomer(o) AND V alidPayment(o)Effect: Charged(o)The precondition axiom:ValidCustomer(o, s) ∧ V alidPayment(o, s) ⇒ Poss(ChargeMoney(o), s)The successor state axiom:Poss(a, s) ⇒ Charged(o, do(a, s)) ⇔ a=ChargeMoneyS(o)∨Charged(o, s) • Elicit non-functional parameters from service level agreement:

  11. Deriving Model Parameters for Abstract Actions (level≥1) Level ≥1:Derive model parameters related to abstract actions from lower level Web process • We need to know successor state axioms and the case notations of lump sum cost, sojourn time distribution and accumulating rate

  12. Deriving Model Parameters for Abstract Actions • Successor state axioms • Let • We haveAnd • The successor state axiom of VerifyOrder(o) becomes: Relation between high-level fluents and low-level fluents Relation between high-level abstract actions and low-level actions

  13. Deriving Model Parameters for Abstract Actions • Lump sum cost K • lump sum cost of the abstract action is the total of lump sum costs of the corresponding primitive actions • Add a new case into the case notation of K kVO = kCase(CheckCustomer(o)) + kCase(VerifyPayment(o)) + kCase(ChargeMoney(o)) New case to be added

  14. Deriving Model Parameters for Abstract Actions • Sojourn time distribution F • Assume the sojourn time of all primitive actions follows Gaussian distribution: fCC(t)=N(t; µcc, σcc), fvo(t)=N(t; µvo, σvo) and fcm(t)=N(t; µcm, σcm) • Linear combination of Gaussian distributions is a Gaussian distribution, the abstract action VerifyOrder also follows Gaussian fvo(t)=N(t; µvo, σvo) where: • Add a new case into the case notation of F New case to be added

  15. Deriving Model Parameters for Abstract Actions • Cost Accumulating Rate C • Accumulated cost of an abstract action is the total accumulated cost of all corresponding primitive actions • Add a new case into case notation of C Given model parameters for abstract actions, composite FO-SMDP can be solved analogously to a primitive FO-SMDP

  16. Architecture of Haley

  17. Interleaved Generation and Execution of Nested Web Process

  18. Performance Evaluation • Comparison with HTN planning and MBP planning on supply chain scenario • Execute the processes generated by three approaches in a simulated environment 1000 times, measure average rewards The performance of HTN approaches ours as the environment becomes less uncertain; Haley provides cost based optimization compared to MBP planner

  19. Performance Evaluation • Comparisons of different decision theoretic planners in the same domain and the collected runtimes • First order representation avoids the explicit state enumeration • Hierarchal decomposition significantly improves the performance

  20. Discussion • Many AI planning based approaches • AI classical planning is not designed to handle WS composition • Assumes deterministic behavior of Web services • Cannot directly operate on WS descriptions in FOL • Does not scale well to large problems • Haley: our hierarchical framework • Stochastic optimization manages uncertainty and delivers optimality • Able to operate directly on WS descriptions in FOL • Exploits hierarchy  scalability • Better performance in uncertain environments • Future work • Incorporate data mediation in Haley

  21. Thank You!Questions?Contact usHaibo Zhao: zhao@cs.uga.eduPrashant Doshi: pdoshi@cs.uga.edu

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