4 th international conference on service oriented computing adaptive web processes using value of...
DESCRIPTION
Web Process Composition Many environments are dynamic Supply Chain Process Start Finish InvokeResponse Spot Market Service Rate of Order Satisfaction Preferred Supplier Service Rate of Order Satisfaction Other Supplier Service Rate of Order Satisfaction Inventory Service Rate of Order Satisfaction ResponseInvoke Inventory satisfaction rate decreases Preferred Supplier may be better choiceTRANSCRIPT
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4th International Conference on Service Oriented Computing
Adaptive Web Processes Using Value of Changed
InformationJohn Harney, Prashant Doshi
LSDIS Lab, Dept. of Computer Science,University of Georgia
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Web Process Composition
Traditional Web process compositions assume static environments
Supply Chain Process
Start Finish Invoke Response
SpotMarket Service
Rate of Order Satisfaction
Preferred Supplier Service
Rate of Order Satisfaction
OtherSupplier ServiceRate of Order Satisfaction
InventoryService
Rate of Order Satisfaction
ResponseInvoke
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Web Process CompositionMany environments are dynamic
Supply Chain Process
Start Finish Invoke Response
SpotMarket Service
Rate of Order Satisfaction
Preferred Supplier Service
Rate of Order Satisfaction
OtherSupplier ServiceRate of Order Satisfaction
InventoryService
Rate of Order Satisfaction
ResponseInvoke
Inventorysatisfaction rate
decreases
Preferred Suppliermay be better choice
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Optimal Web Process Composition
• Underlying objective– Web process optimality
• Depends on how accurately the environment is captured
• Requires finding any changes that may have occurred
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Motivating Scenario – Supply Chain
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Motivating Scenario – Supply Chain
• How does process environment change?– Example: Supply Chain (Inventory service)
• Rate of satisfaction of a supplier service – Eg Inventory satisfaction rate decreases or increases
• Cost of using a service– Cost of the Inventory service decreases or increases
• Other parameters (response time, QoS, etc)
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Possible Adaptation Approaches
• Do Nothing (Ignore the changes)– Advantages
• Simple• No additional cost or computational
overhead of adaptation– Disadvantages
• Sub-optimal Web process – Web process can do better
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Possible Adaptation Solutions• Query a random provider for relevant
information (eg. Inventory)– 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
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Overview of Our Approach• VOC – Value of Changed Information
– 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)
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Overview of Our 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
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Web Process Composition Using MDPs• Markov Decision Processes (MDP) (see JWSR 05)
– Definition: M = (S, A, T, C) S : States, A: Actions,
• Actions may be non-deterministic T: Transition function,• States are fully observable S x A (S)
C: Cost function S x A Real
• Perform stochastic optimization using Dynamic Programming
• Value function heuristic :
• Optimal Policy n : S A– (Minimize expected cost)
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Web Process Composition Using MDPsS: Feature-based state space using propositions
– E.g. Mftg. Inventory Availability Yes|No|Unknown
A: WS invocations – E.g. Check Mftg. Inventory Status
Check Preferred Supplier Status
T: An estimate of the “ground truth” probabilities– E.g. T(Mftg. Inventory Avail = Yes | Check Mfg. Inventory
Status, Mftg. Inventory Availability = Unknown) = 0.33
C: Costs may be obtained through costing analysis
Π*: Determines which service to invoke at a particular state
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Formalizing VOC• Supply Chain Example
– Querying Transition function T (satisfaction rate of suppliers in supply chain)
– Changed Transition function – T’(.|a,s’)– Current Policy Value - Vπ(s|T’) – Best Policy Value - Vπ*(s|T’)
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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
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Manufacturer’s BeliefsExample - Beliefs of Order Satisfaction
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Adaptive Web Process Composition
…Prov 1 Prov 2 Prov n
VOC VOC VOC
Keep current policy
Query Provider Re-solve policy
if needed
1. Calculate VOC for each service provider involved in Web process
2. Find provider whose changed parameter induces the greatest change in policy (VOC*)
3. Compare VOC* to cost of querying
VOC* < Cost of Querying
VOC* > Cost of Querying
*
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Our Services Oriented Architecture
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Empirical Results• Simulated volatile Supply Chain & Patient
Transfer scenarios for:– Do Nothing
• keeping the same process– Query random provider
• Obtaining information from one provider at each state
• Reformulate composition => Resolve policy– VOC
• VOC for determining if query is needed• Reformulate composition if need be
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Empirical Results• Measured the average process cost over a range of query cost
values– VOC queries selectively -- query random strategy cost grows at a larger
rate than VOC– VOC performs no worse that the do nothing strategy
Supply Chain Web Process Patient Transfer Web Process
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Discussion• Web Process environments are dynamic
– Processes must adapt to changes in environment to remain optimal
– Obtaining the revised information is crucial but may be costly
• VOC approach– Obtains revised information expected to be
useful– Avoids unnecessary queries
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Future Work• VOC calculations are computationally
expensive– Knowledge of service parameter guarantees
may be used to eliminate unnecessary VOC calculations
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Thank you
Questions