Toward Optimal and Efficient Self-Adaptation in Large Web Processes

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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

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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

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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
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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
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Abstract Processes and Service Managers

• Our architecture
• Two tiers
• Resources Layer
• Control Layer

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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
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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

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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
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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
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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

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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

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Related Work

• Verification of correctness of manual changes to
  control flow
• Adept (ReichertDadam98), Workflow inheritance
  (AalstBasten02), 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

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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

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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)
  (HarneyDoshi,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)

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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

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Manufacturers Beliefs For Supply Chain
Example - Beliefs of Order Satisfaction
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Adaptive Web Process Composition
1. SM calculates VOC for each service provider
involved in Web process
Prov 1
Prov 2
Prov n

VOC
VOC

VOC
2. PM finds provider whose changed parameter
induces the greatest change in process (VOC)
VOC lt Cost of Querying
VOC gt Cost of Querying
3. Compare VOC to cost of querying
Keep current process
Query Provider Re-compute process if needed
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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
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Discussion

• Understanding dynamic environments is crucial
• Categorizations needed
• Data and component volatility
• Expected (with probabilities known apriori) 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

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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
  (HarneyDoshi, WWW 07)
• Other approaches needed

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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

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Thank YouQuestions