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
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
Understanding Volatility
•
Data Volatility
– Atypical input and execution data
• E.g.. delay in satisfying order
adverse drug reaction
data volatility
– New knowledge
• E.g.. New drug alert
Component Volatility
– Change in the state of the process
participants
component
volatility
• E.g.. Web service failure or abnormal behavior
expected unexpected
(with some chance)
•
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
Abstract Processes and Service Managers
• Pre-specified abstract processes
– Ordering of activities
– Inter-activity constraints: E.g. Coordination constraints
Heart Failure
Clinical Pathway
• Process and Service Managers
Abstract Processes and Service Managers
• Our architecture
– Two tiers
• Resources Layer
• Control Layer
A Framework for Studying Adaptation
• Two criteria for evaluating approaches
– Cost-based optimality
– Computational efficiency
Centralized
Adaptation
Hybrid approaches
Decentralized
Adaptation
Decreasing Optimality
Decreasing Computational Efficiency
• Formalize adaptation as a decision problem
– Two general choices
• Ignore the change
• React to the change
– Example methodology: Markov decision processes (MDP)
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
Establishing the Ends of the Spectrum
• Centralized adaptation to
expected data volatility
• Example: M-MDP method
Computer assembly
(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
Establishing the Ends of the Spectrum
• Decentralized adaptation to
expected data volatility
• Example: MDP-CoM method
Computer assembly
(Verma, Doshi et al. ICWS 06)
• Challenge: Satisfying
coordination constraints
Properties:
• Scalable to multiple SMs
• Not optimal
Coordination Mechanism
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
Some Experimental Results
•Wait out the delay
•Change the supplier
2500
Average Cost($)
Adapting to delay in supply
chain
• Choices
M-MDP
Penalty of delay = $200
Random
2100
Hyb. MDP
MDP-CoM
1700
1300
900
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Probability of delay
Runtime for MDP-CoM remains fixed
as number of activities increases
•Decentralized adaptation is
parallelizable
2500
Average Cost($)
M-MDP incurs the least average cost
MDP-CoM the most
M-MDP
Penalty of delay = $400
Random
2100
Hyb. MDP
MDP-CoM
1700
1300
900
0.1
0.2
0.3
0.4
0.5
Probability of delay
0.6
0.7
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
Unexpected Data Volatility
•
Example
–
–
•
Rate of order satisfaction may change arbitrarily
Cost of service may change arbitrarily
Research Challenges
1. 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
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)
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
Manufacturer’s Beliefs For Supply Chain
Example - Beliefs of Order Satisfaction
Adaptive Web Process Composition
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*)
Prov 1
VOC
VOC* < Cost of
Querying
Prov 2
…
VOC *
VOC* > Cost of
Querying
3. Compare VOC* to cost
of querying
Keep current
process
Query Provider
Re-compute process if
needed
Prov n
VOC
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
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
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
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
Thank You
Questions