Optimal Adaptation in Web
Processes with Coordination
Constraints
Kunal Verma, Prashant Doshi, Karthik Gomadam,
John A. Miller, Amit P. Sheth
LSDIS Lab, Dept of Computer Science, University of Georgia
Outline
• Motivation
• Process Adaptation
• Empirical Evaluation
• Conclusions, Related Work and Future
Agenda
Motivation
• Evolution of business needs drives IT innovation
• Initial focus on automation led to workflow technology
• In order to facilitate efficient inter-organizational processes
distributed computing paradigms were developed
– CORBA, JMS, Web Services
• The current and future needs include:
– Creating highly adaptive process that react to changing
conditions
• Focus on real time events and data – RFID and ubiquitous devices
– Have the ability to quickly collaborate with new partners
– Aligning business goals and IT processes
Motivation
“Each enterprise will measure and aspire to its own unique level of
• dynamism
Current based
Toolson
focus
on allowing
businesses
to have
its individual
purpose.
It is about being
nimblegreater
and
dynamism
and
agilitybusiness platform can respond faster, and
adaptable.
A fully
integrated
completely,
to change.
Whether
it involves
fulfilling
a new mandate
or
– Microsoft
Dynamics,
IBM
Websphere
Business
Integration,
SAP
Netweaver
embracing
a new market opportunity. Some organizations will push the
• All
of these Current
focus on dynamic
andfor
agility
through
human
envelope,
automating
event-triggered
responses
highly
integrated
interaction
using
GUIs
closed-loop
processes,
setting
the stage for self-optimizing systems.”
• All of them list SOA (WS) as a technology for realization
Sandra Rogers, White Paper: Business Forces Driving Adoption of Service Oriented
Sponsored by: SAP AG
• Architecture,
The future
– Move focus to greater automation
• Capture domain knowledge and declaratively specify criteria for
process configuration (Dynamic process configuration)
• Add decision making support to process execution tools for
process adaptation (Process Adaptation)
SOA Maturity Model
Adaptive/Autonomic
Levels of autonomic maturity
System
monitors,
correlates and
takes action
Established
Standards
Centralized
tools and
manual
analysis
Manual
Analysis
No Established
Standards
Correlation
and
guidance
Dynamic
Business
policy based
management
Motivating Scenario
• Consider a simplified supply chain process
of a computer manufacturer
– Most parts are multiple sourced (overseas and
internal suppliers)
• Suppliers characterized as preferred or secondary
• Overseas goods cheaper but greater lead times
– There often exist part compatibility constraints
• Choosing a certain motherboard restricts choices of
RAMs, processors
– Usually important to maintain production
schedule in the presence of delayed orders
Process Adaptation
• Ability to adapt the processes to external events
– Expected events
– Unexpected events
• Two kinds of failures
– Failures of physical components like services, network
• Can replace services using dynamic configuration
– Logical failures like violation of SLA
constraints/Agreements such as Delay in delivery, partial
fulfillment of order
• Need additional decision making capabilities
Process Adaptation
Adaptation Problem
Optimally adapt to events like delays in ordered
goods
Conceptual Approach
1. Maintain states of the process – normal states,
error states, goal states
2. Capture costs while transitioning from error states
to goal state
3. Ability to decide optimal actions on the basis of
state
Process Adaptation
• Research Challenges
– Creating a model to recover from failures and handle external
events
– Model must deal with two important factors
• Uncertainty about when a failure occurs
• Cost based recovery
• Scenario
– After order for MB and RAM are placed, they may get delayed
– The manufacturer may have severe costs if assembly is halted
– It must evaluate whether it is cheaper to cancel/return and
reorder or take the penalty of delay
– Caveat: possible that reordered goods may be delayed too
New Framework
• Introduce a framework within which to
study process adaptation
• Two criteria
– Cost-based optimality
– Computational Efficiency
Centralized
Adaptation
Hybrid approaches
Decreasing Optimality
Decreasing Computational Efficiency
Decentralized
Adaptation
High Level Architecture
Event from service
Web Services
Adaptation
Module
Service
invocation
METEOR-S
MIDDLEWARE
Process
and
Service
Managers
MDP
Configuration/Invocation
Response Message
Entities
Configuration
Module
Discovery
Constraint
Analysis
Configuration/Invocation
Request Message
Workflow Engine
(IBM BPWS4J)
Process Manager (PM):
Responsible for global process
configuration
Service Manager (SM):
Responsible for interaction of
process with service
Configuration Module (CM):
Discovery and constraint
analysis
Adaptation Module (AM):
Process adaptation from
exceptions/events
Deployed Web Process
Modeling Decision Making Process of
Service Managers using MDPs
Each Service Manager is controlled by a MDP
SM = <S, A, PA, T, C, OC> , where
•
S is the set of local states of the service manager.
•
A is the set of actions of the service manager. The actions include invoking Web
service operations and calling the configuration manager.
•
PA : S → A is a function that gives the permissible actions of the service manager
from a particular state.
•
T : S × A × S → [0, 1] is the local Markovian transition function. The transition
function gives the probability of ending in a state j by performing action a in state i.
•
C : S × A → R is the function that gives the cost of performing an action from some
state of the service manager.
•
OC is the optimality criterion. We minimize the expected cost over a finite number of
steps, N, also called the horizon.
Policy Computation
• The optimal action at each state is represented using a
policy.
• In order to compute the policy, a value is associated to
each state.
– The value represents long term expected cost of performing
the optimal action from that state and is calculated the
following dynamic programming equation.
Vn ( s )  min Qn ( s,a )
aPA( s )
Qn ( s,a )  C( s,a )    T( s' | s,a )Vn1( s')
s'
The policy pi : S × N → R is then computed as:
pin ( s )  arg min Qn ( s,a )
aPA( s )
N is the number of steps to go and Gamma is the discount factor
Algorithm developed by Bellman in 57
Marginalizing events
Generating States using preconditions
and effects
Actions
Pre: Ordered = False
Events
Operation: Order
Post: Ordered = True
Pre: Ordered = True &
Received = false
Chance Variables
Ordered
Event: Delayed
Pre: Ordered = True &
Received = false
Received
Post: Delayed=True &
Ordered = True
Operation: Cancel
Post: Canceled=True &
Ordered = false
Pre: Ordered = True &
Received = True
Operation: Return
Post :Returned = True &
Ordered = false and
Received = false
Delayed
Pre: Ordered = True &
Received = false
Event: Received
Post: Received = True
Cancelled
Returned
Generated State Transition Diagram
State
No.
Values of Boolean
variables
Explanation
O
O
1
2
3
<O C R Del Rec 
Ordered
<O C R Del Rec 
Ordered and Canceled
<O C R Del Rec 
W
O
s1
C
s2
Ordered and Delayed
4
<O C R Del Rec 
Ordered, Received and
Returned
5
<O C R Del Rec 
Ordered, Delayed and
Cancelled
6
<O C R Del Rec 
Ordered, Delayed, Received
and Returned
7
<O C R Del Rec 
Ordered, Delayed and
Received
8
<O C R Del Rec 
Ordered and Received
O
Del
s3
C
s5
s6
R
W
Rec
s4
Rec
R
s8
s7
W
W
Costs and Probabilities
• Costs of ordering taken from configuration
module
– From first two service sets
• Optimal supplier and alternate supplier
• Probability of delay and cost of returning
and canceling taken from supplier policy
– Can be represented using WS-Policy or WSAgreement
Supplier Policy
– The supplier gives a probability of 55% for delivering the
goods on time.
– The manufacturer can cancel or return goods at any
time based on the terms given below.
• If the order is delayed because of the supplier, the order
can be cancelled with a 5% penalty to the manufacturer.
• If the order has not been delayed, but it has not been
delivered yet, it can be cancelled with a penalty of 15% to
the manufacturer.
• If the order has been received after a delay, it can be
returned with a penalty of 10% to the manufacturer.
• If the order has been received without a delay, it can be
returned with a penalty of 20% to the manufacturer.
Costs and Probabilities
Current State
<O C R Del Rec 
Action
NOP
Next State
<O C R Del Rec 
Cost
0
<O C R Del Rec 
CANCEL
<O C R Del Rec 
150
<O C R Del Rec 
DEL
<O C R Del Rec 
0
<O C R Del Rec 
RECEIVE
<O C R Del Rec 
0
<O C R Del Rec 
ORDER
<O C R Del Rec 
100
<O C R Del Rec 
NOP
<O C R Del Rec 
<O C R Del Rec 
CANCEL
<O C R Del Rec 
DelayCost =
{200, 300, 400}
50
<O C R Del Rec 
RECEIVE
<O C R Del Rec 
0
<O C R Del Rec 
ORDER
<O C R Del Rec 
100
<O C R Del Rec 
ORDER
<O C R Del Rec 
100
<O C R Del Rec 
ORDER
<O C R Del Rec 
100
<O C R Del Rec 
CANCEL
<O C R Del Rec 
150
<O C R Del Rec 
NOP
<O C R Del Rec 
0
<O C R Del Rec 
RETURN
<O C R Del Rec 
200
<O C R Del Rec 
NOP
<O C R Del Rec 
0
O
O
W
O
s1
C
0.45
Rec
R
Rec
s4
0.35
0.85
s6
W
s3
C
s5
O
Del
s2
s7
W
R
s8
W
Handling Coordination Constraints
• Since the RAM and Motherboard must be
compatible, the actions of service managers
(SMs) must be coordinated
• For example, if MB delivery is delayed, and MB
SM wants to cancel order and change supplier,
the RAM SM must do the same
• Hence, coordination must be introduced in SMMDPs
Centralized Approach
• State space created by Cartesian
product of transition diagrams
• Joint actions from each state
• Transition probability created by
multiplying states
• Costs created by adding cost per
action from each state
– Compatible actions given rewards
– Incompatible actions given penalties
• Optimal but exponential with
number of manager
Decentralized Approach
• Simple coordination
mechanism
• If one service manager
changes suppliers
– All dependent managers
must change suppliers
• Low complexity but suboptimal
Hybrid Approach
• If the policy of some SM dictates it to change suppliers, the
following actions happen:
–
–
it sends a coordinate request to PM
PM gets the current cost of changing suppliers or current
optimal action by polling all SMs
• It takes the cheapest action (change supplier or continue)
• A bit like decentralized voting- will change suppliers if
majority are delayed
• It mirrors performance of centralized approach and has
complexity like the decentralized approach
Evaluating Process Adaptation
• Evaluation with the help of the supply chain
scenario
• Two main parameters used for the evaluation
– Probability of Delay – (probability that an item ordered
from a supplier will be delayed)
– Penalty of Delay – (cost for the manufacturer for not
reacting to delay)
• Total process cost = $1000 and cost of changing
suppliers (CS) =$200
Evaluating Adaptation
Cost of Waiting = 200
2500
M-MDP
Random
2100
Average Cost
Hyb. MDP
MDP-CoM
1700
KEY
M-MDP: Centralized
Random: Random process
(changes suppliers for 50% of
delays)
1300
Hyb. Com: Hybrid
MDP-Com: Decentralized
900
0.1
0.2
0.3
0.4
0.5
Probability of delay
0.6
0.7
Evaluating Adaptation
Cost of Waiting = 300
2500
M-MDP
Random
2100
Average Cost
Hyb. MDP
MDP-CoM
1700
1300
900
0.1
0.2
0.3
0.4
0.5
Probability of delay
0.6
0.7
Evaluating Adaptation
Cost of Waiting = 400
2500
M-MDP
Random
Average Cost
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
Observations
• Results
– For Penalty = 200 (cost of CS = cost of delay), MDP always
waits
– For Penalty = 300, 400 (cost of CS < cost of delay), MDP
changes at lower prob., waits at higher prob.
• Conclusions
– Thus MDP makes intelligent decisions and outperforms random
adaptation that changes suppliers 50% of the time it is
delayed
– Centralized MDP performs the best, followed by Hybrid MDP
Related work
• Focus on correctness of changes to control flow structure
– Adept[1], Workflow inheritance [2], METEOR
• Use of ECA rules [3] to automatically make changes
• Change of service providers based on migration rules in EFlow [4]
• We extend previous work in this area by using:
– Cost based adaptation
– Coordination Constraints across services
[1] M. Reichert and P. Dadam. Adeptflex-supporting dynamic changes of workflows without losing control.
Journal of Intelligent Information Systems, 10(2):93–129, 1998
[2] W. van der Aalst and T. Basten. Inheritance of workflows: an approach to tackling problems related to
change. Theoretical Computer Science, 270(1-2):125–203, 2002.
[3] R. Muller, U. Greiner, and E. Rahm. Agentwork: a workflow system supporting rule-based workflow
adaptation. Journal of Data and Knowledge Engineering, 51(2):223–256, 2004.
[4] Fabio Casati, Ski Ilnicki, Li-jie Jin, Vasudev Krishnamoorthy, Ming-Chien Shan: Adaptive and Dynamic
Service Composition in eFlow. CAiSE 2000: 13-31
Conclusions and Future Work
• Showed the utility of Markov Decision Processes for optimal
adaptation of Web processes
– Adaptation is need to handle logical failures and events
– Whether to adapt or not depends on the cost of the failure
• For this evaluation it was the cost of the delay
• In the real world things often go wrong or not as expected
– Earlier processes were static or real time events were not available as
easily
– Many researchers/industry vendors seeking to create adaptive
business process frameworks
– This is one of the first works that provides cost based adaptation
• Future Work
– Move towards autonomic Web processes