Optimal Adaptation in Web Processes with Coordination Constraints

1. 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

2. Outline • Motivation • Process Adaptation • Empirical Evaluation • Conclusions, Related Work and Future Agenda

3. 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

4. Motivation “Each enterprise will measure and aspire to its own unique level of dynamism based on its individual purpose. It is about being nimble and adaptable. A fully integrated business platform can respond faster, and completely, to change. Whether it involves fulfilling a new mandate or embracing a new market opportunity. Some organizations will push the envelope, automating event-triggered responses for highly integrated closed-loop processes, setting the stage for self-optimizing systems.” Sandra Rogers, White Paper: Business Forces Driving Adoption of Service Oriented Architecture, Sponsored by: SAP AG • Current Tools focus on allowing businesses to have greater dynamism and agility • Microsoft Dynamics, IBM Websphere Business Integration, SAP Netweaver • All of these Current focus on dynamic and agility through human interaction using GUIs • All of them list SOA (WS) as a technology for realization • 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)

5. SOA Maturity Model Adaptive/Autonomic

6. Levels of autonomic maturity System monitors, correlates and takes action Dynamic Business policy based management Established Standards Correlation and guidance Centralized tools and manual analysis Manual Analysis No Established Standards

7. 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

8. 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

9. Process Adaptation Adaptation Problem Optimally adapt to events like delays in ordered goods • Conceptual Approach • Maintain states of the process – normal states, error states, goal states • Capture costs while transitioning from error states to goal state • Ability to decide optimal actions on the basis of state

10. 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

11. New Framework • Introduce a framework within which to study process adaptation • Two criteria • Cost-based optimality • Computational Efficiency Decentralized Adaptation Centralized Adaptation Hybrid approaches DecreasingOptimality Decreasing Computational Efficiency

12. High Level Architecture • Entities • 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

13. 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.

14. 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. The policy pi : S × N → R is then computed as: N is the number of steps to go and Gamma is the discount factor Algorithm developed by Bellman in 57

15. Marginalizing events

16. Generating States using preconditions and effects Actions Chance Variables Events

17. Generated State Transition Diagram

18. 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 WS-Agreement

19. 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.

20. Costs and Probabilities

21. 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 SM-MDPs

22. 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

23. Decentralized Approach • Simple coordination mechanism • If one service manager changes suppliers • All dependent managers must change suppliers • Low complexity but sub-optimal

24. 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

25. 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

26. Evaluating Adaptation KEY M-MDP: Centralized Random: Random process (changes suppliers for 50% of delays) Hyb. Com: Hybrid MDP-Com: Decentralized

27. Evaluating Adaptation

28. Evaluating Adaptation

30. 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

31. 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 E-Flow [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

32. 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