Toward Optimal and Efficient Self-Adaptation in Large Web Processes
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Transcript of 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 DoshiAssistant 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 FailureClinical 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 EfficiencyDecreasing Optimality
Centralized Adaptation
DecentralizedAdaptationHybrid approaches
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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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Penalty of delay = $400
900
1300
1700
2100
2500
0.1 0.2 0.3 0.4 0.5 0.6 0.7
Probability of delay
Av
era
ge
Co
st(
$) M-MDP
Random
Hyb. MDP
MDP-CoM
Penalty of delay = $200
900
1300
1700
2100
2500
0.1 0.2 0.3 0.4 0.5 0.6 0.7
Probability of delayA
vera
ge
Co
st($
)
M-MDP
Random
Hyb. MDP
MDP-CoM
Some Experimental ResultsAdapting to delay in supply chain• Choices
•Wait out the delay•Change the supplier
M-MDP incurs the least average costMDP-CoM the most
Runtime for MDP-CoM remains fixedas number of activities increases•Decentralized adaptation is parallelizable
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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
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Unexpected Data Volatility• Example
– Rate of order satisfaction may change arbitrarily– Cost of service may change arbitrarily
• Research Challenges1. How to be cognizant of the change
2. 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) (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)
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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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Manufacturer’s Beliefs For Supply Chain
Example - Beliefs of Order Satisfaction
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Adaptive Web Process Composition
…Prov 1 Prov 2 Prov n
VOC VOC VOC
Keep current process
Query Provider Re-compute process if
needed
1. SM calculates VOC for each service provider involved in Web process
2. PM finds provider whose changed parameter induces the greatest change in process (VOC*)
3. Compare VOC* to cost of querying
VOC* < Cost of Querying
VOC* > Cost of Querying
*
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Empirical ResultsMeasured 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 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
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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 (Harney&Doshi, 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 You
Questions