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Improved Adaptation of Web Service Compositions Using Value of Change Information

Improved Adaptation of Web Service Compositions Using Value of Change Information. Girish Chafle Sumit Mittal Biplav Srivastava IBM India Research Laboratory. Prashant Doshi John Harney LSDIS Lab, University of Georgia. International Conference on Web Services 2007. Outline. Background

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Improved Adaptation of Web Service Compositions Using Value of Change Information

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  1. Improved Adaptation of Web Service Compositions Using Value of Change Information Girish Chafle Sumit Mittal Biplav Srivastava IBM India Research Laboratory Prashant Doshi John Harney LSDIS Lab, University of Georgia International Conference on Web Services 2007

  2. Outline • Background • Web Process Adaptation • A-WSCE System • Overview of VOC • A-WSCE with VOC • Evaluation • Contributions and Future Work

  3. Background • Business environments are volatile/dynamic • Data Volatility • e.g. QoS parameters change during lifetime • Component Volatility • e.g. Components fail • Examples • Supply Chain service provider’s rate of order satisfaction may decrease • Cost of using a service increases due to increase in supporting costs • Service response time increases due to network difficulties

  4. New service capabilities Specify new service requirement New service providers Design the workflow Network / environment changes Select Service Providers Deploy the service Web Process Adaptation • Composition of Web Processes • Web Process composition using existing component services • Deployment and execution on run-time infrastructure • Adaptation of Web Process to incorporate changes in environment • Component service failure • Changing QoS of component services • Availability of new service instances • Availability of new service functionalities

  5. Web Process Adaptation • Many Web service compositions do not cater to volatility • At times assume static environments • Service providers’ QoS parameters are assumed fixed • Adaptation in practice • Primary aim to compose correct and optimal workflow • Adaptation of workflow is viewed as an afterthought • Fail to consider potential overheads of adaptation while formulating the workflow • Can we consider possibility of adaptation while composing workflow?

  6. A-WSCE Overview (ICWS ’06) • Use of Staged Approach for Service Composition • Decompose the specification into functional and non-functional requirements • Differentiate between Web service types and Web service instances • Logical Composition: Create the desired functionality using service types • Physical Composition: Optimize non-functional requirements and bind service instances • Create alternatives to facilitate adaptation • Intelligently select alternatives at each stage based on operating conditions

  7. Service Developer Available Types Available Instances Service Type Registry Instance Registry QoS Registry Type Instance QoS Feedback Feedback Feedback Execution Environment Template Generator K Templates L Workflows 1workflow Workflow Generator Runtime Selector CALLBACK CALLBACK CALLBACK Logical Manager Physical Manager Runtime Manager A-WSCE System Architecture

  8. A-WSCE Summary • Considers possibility of adaptation at the time of composition • Exploits redundancy as a way to adapt workflows by identifying multiple diverse workflows that achieve the same goal • Selects optimal workflows for execution • Handles data and component volatility by switching to alternate workflows using Staged Approach

  9. A-WSCE Limitations • Utilizes simple strategy of frequently querying all service instances for the latest QoS values • Becomes an issue when queries are expensive to perform • May not result in significant gains in the QoS values leading to costly switches Can we do intelligent querying?

  10. Overview of VOC • VOC – Value of Changed Information (ICSOC ’06, WWW ‘07) • Decides if obtaining information is • Useful • Will it induce a change in optimality of Web process? • Cost-efficient • Is the information worth the cost of obtaining it? • Measures how “badly” the current process is performing in changed environment • Determines the expected impact of the changed information on the workflow

  11. Normal (Gaussian) Distribution 0 2 3.6 5.2 6.8 8.4 10 11.6 13.2 14.8 16.4 18 Calculating VOC • VOC of an individual service provider is 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 • For example cost:

  12. Incorporating VOC in A-WSCE • Compute the aggregate VOC for each workflow • Signifies how much the entire workflow is expected to change • Use the VOC value to determine whether to obtain the latest QoS values of service instances • Compare against a threshold • If decides to query, fetch the latest QoS values of instances, and re-compute the aggregate QoS along with VOC values • Use this value in subsequent iterations to compare against threshold VOC can be easily incorporated in the Runtime stage

  13. Calculating VOC • VOC of each QoS parameter (ex. cost) is • Aggregated over the workflow • Normalized to allow comparison across different workflows • Aggregate VOC of the workflow across all dimensions Avail. Cost Response time

  14. Service Developer Available Types Available Instances Type Instance QoS Service Type Registry Instance Registry QoS Registry Feedback Feedback Feedback Execution Environment K Templates L Workflows 1workflow Workflow Generator Runtime Selector Template Generator CALLBACK CALLBACK CALLBACK Logical Manager Physical Manager Runtime Manager Modified A-WSCE Algorithm A-WSCE System while (true) Receive Monitoring Info every TM period Update QoS registry if (instance failure results in failure of executing workflow) call RuntimeManager.RuntimeSelector endif Call RuntimeManager every TR period Call PhysicalManager every TP period Call LogicalManager every TL period end while PhysicalManager Start If (all instances of a type are down) Inform LogicalManager endif Call WorkflowGenerator If (no executable workflow can be created) Call LogicalManager.TemplateGenerator Call WorkflowGenerator Compute VOC for each workflow generated endif End

  15. Service Developer Available Types Available Instances Type Instance QoS Service Type Registry Instance Registry QoS Registry Feedback Feedback Feedback Execution Environment K Templates L Workflows 1workflow Workflow Generator Runtime Selector Template Generator CALLBACK CALLBACK CALLBACK Logical Manager Physical Manager Runtime Manager Modified A-WSCE Algorithm RuntimeManager Start Send QoS updates and instance failure to PhysicalManager for (each executable workflow available) if( VOC > threshold) Query latest QoS values for instances Recompute aggregate QoS of workflow Recompute aggregate VOC of workflow endif endfor Call RuntimeSelector If (no executable workflow available) Call PhysicalManager.WorkflowGenerator Call RuntimeSelector endif End LogicalManager Start Call TemplateGenerator If (no new template can be created) Seek user intervention endif End

  16. Evaluation • Aim: Evaluate the effectiveness of VOC in the context of adaptation in A-WSCE • How does VOC compare with other strategies of querying • What is the effect of query cost on the performance • How does intelligent querying using VOC help in improving the system stability • Experiments conducted on a simulated set-up • Systems evaluated • A-WSCE with always query (previous approach) • A-WSCE with VOC • A-WSCE with random query

  17. Empirical Results: Aggregate QoS

  18. Empirical Results: Number of Switches

  19. Empirical Results: Summary • VOC improves the performance of A-WSCE system • Improves QoS • Improves stability • VOC does selectivebut intelligentquerying

  20. Contributions and Future Work • Contributions • Explored optimal adaptation in the presence of data and component volatility • Incorporated VOC in the A-WSCE framework • Used VOC as a mechanism to enable intelligent querying for revised QoS in the A-WSCE framework • Future Work • Use VOC at other stages of the A-WSCE system • Incorporate at the granularity of individual instances • Enhance the VOC model to include other factors like likelihood of one instance changing its QoS values more frequently with respect to other instances

  21. Thank You! Questions

  22. References • Synthy: A System for End to End Composition of Web Services, Journal of Web Semantics 2005 • A Service Creation Environment based on End to End Composition of Web Services, WWW 2005 • Demo presented at AAAI Intelligent System Demonstration • Domain-Dependent Parameter Selection of Search-based Algorithms Compatible with User Performance Criteria, AAAI 2005 • Information Modeling for End to End Composition of Web Services, ISWC 2005 • The Synthy Approach for End to End Web Services Composition: Planning with Decoupled Causal and Resource Reasoning, AAAI 2006 • A Framework for Analyzing Web Services Composition and Execution Approaches (Early version in Evaluating Planning based Approaches for End to End Composition and Execution of Web Services, AAAI 2005 Workshop on Planning and Scheduling), IBM Research Report. • User-Driven Search Control in Contingent Planning and an Application IBM Research Report RI02006. • Finding Inter-related Plans, IBM Research Report. • An Integrated Development Environment for Semantic Web Service Composition, IBM Research Report • Adaptive Web Processes using value of changed information, ICSOC 2006 • Speeding up adaptation of web service compositions using expiration times, WWW 2007

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