A Hierarchical Framework for Composing Nested Web Processes Haibo Zhao, Prashant Doshi LSDIS Lab, Dept. of Computer Science, University of Georgia 4 th International Conference on Service Oriented Computing

Introduction Web process  Business processes with Web services as components Existing approaches to Composition: AI planning  Classical planning techniques  Golog (McIlraith2001), MBP-based planning (Traverso2003), HTN planning (Wu2003), Synthy (Srivastava2005)  Decision-theoretic planning  MDP (Doshi2004) Limitations  Classical planning assumes deterministic behavior of Web services  Guarantees correctness but not optimality  Existing approaches do not scale well to large processes Our approach  Hierarchy in Web processes to address the scalability problem  A hierarchical semi-MDP to model the hierarchy  Stochastic SMDP model to handle uncertainties and optimality

Motivating Scenario 1 Order handling in supply chain singly nested

Motivating Scenario 2 Patient Transfer Pathway doubly nested

Our Approach Level 0 Composition using primitive SMDP Level 0 Composition using primitive SMDP Level 1 Composition using composite SMDP Abstract actions

Semi-MDP (SMDP) SMDP = S: set of all states Capture feature-based state space (factored into variables) E.g. isOrderValid  Yes/No/Unknown A: set of actions, some temporally extended Model Web service invocations or operations  Level 0 – action is the invocation of a primitive WS E.g. receiveOrder  Level > 1 – Abstract action is the invocation of a lower-level Web process E.g. verifyOrder T: transition function, T: S X A  Δ(S) Uncertain effects with probabilities E.g. T( inventoryAvail = Yes | Check Inventory Status, inventoryAvail = Unknown) = 0.3 K: lump sum reward/cost, K: S X A  R WS invocation or usage reward/cost F: sojourn time distribution, F: S X A  Δ(t)  Level 0 – uncertain response time of Web services  Level > 1 – uncertain runtime of lower level Web process C: reward/cost accumulating rate, C: S X A  R Reward/cost rate (cost per time unit) of using Web services s0 : initial state

Solving SMDPs Expected Utility of state s: Quantitative measure of immediate effects and long-term effects of actions Where: Solving a SMDP is maximizing the expected utility of each state, Solution techniques include: Value Iteration, Policy Iteration and Linear Programming The solution is a policy: Mapping from states of process environment to actions –More robust than a sequence of Web service invocations Execution of a policy: 1. Determine the current state s 2. Invoke WS given by action a based on the policy 3. Repeat 1-2 until the goal is achieved

Elicitation of Model Parameters Level 0: Model parameters may be obtained from WSDL\SAWSDL, OWL- S descriptions of Web services, and WS-Agreements Level >1: Derive model parameters related to abstract actions from lower level Web process Specifically, we want to know transition function T, lump sum reward K, sojourn time distribution F, and accumulating rate C

Abstract Actions and Variable Correspondence Abstract action “Verify Order”( ) is composed of “Check Customer”(a cc ), “Verify Payment”(a vp ), and “Charge Money”(a cm ) Actions affect only certain variables Correspondence between high-level and lower-level preconditions and effects  For example: High-level variable OrderValid=? Corresponding low-level variable values OrderValid = UCustomerValid=U and PaymentVerified=U and AccountCharged=U OrderValid = YCustomerValid=Y and PaymentVerified=Y and AccountCharged=Y OrderValid = NCustomerValid=N or PaymentVerified=N or AccountCharged=N

Deriving Transition Function T for Abstract Actions Correspondence: E.g. The transition from (OV=U) to (OV=Y) High-level variable OV=?Corresponding low-level variables OrderValid = UCustomerValid=U and PaymentVerified=U and AccountCharged=U OrderValid = YCustomerValid=Y and PaymentVerified=Y and AccountCharged=Y OrderValid = NCustomerValid=N or PaymentVerified=N or AccountCharged=N

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

Deriving Model Parameters for Abstract Actions Sojourn time distribution F –Assume the sojourn time of all primitive actions follows Gaussian distribution: F CC : N(t; µ cc, σ cc ), F vo : N(t; µ vo, σ vo ) and F cm : N(t; µ cm, σ cm ) Linear combination of Gaussian distributions is a Gaussian distribution The abstract action VerifyOrder also follows Gaussian F vo : N (t; µ vo, σ vo ) where:

Deriving Model Parameters for Abstract Actions Accumulating Cost Rate C –Accumulated cost of an abstract action is the total accumulated cost of all corresponding primitive actions –Where: E cc (F), E vp (F) and E cm (F) are expected sojourn time Given model parameters for abstract actions, composite SMDP can be solved analogous to a primitive SMDP

System Architecture

BPEL Snippet

Interleaved Generation and Execution of Nested Web Process

Performance Evaluation Methodology − Comparison with HTNs (Wu 2003) on two scenarios − Run the processes generated by two approaches in a simulated environment 1000 times − Measure average reward and standard deviation The performance of HTN approaches ours as the environment becomes less uncertain

Discussion Many AI planning approaches  AI classical planning is not designed to handle WS composition  Assumes deterministic behavior of Web services  Does not scale well to large problems Our hierarchical framework  Stochastic optimization manages uncertainty and delivers optimality  Exploits hierarchy  scalability  Better performance in uncertain environments Future work  Integrate first-order logic to manage state space explosion

Thank You! Questions?

Outline Introduction Motivating scenarios Semi-Markov decision process (SMDP) Composing nested web processes using Hierarchical SMDP System architecture Experiment & Discussion

Composing Web Processes Using H-SMDP