Dynamic Workflow Composition: Using Markov Decision Processes

doi:10.4018/jwsr.2005010101

Reference Hub

This research has been cited in:

Conference
MDP and learning based approach for ubiquitous services composition2010 IEEE Globecom Workshops10.1109/GLOCOMW.2010.5700224
Chapter
Web Services Discovery and Constraints CompositionWeb Reasoning and Rule Systems10.1007/978-3-540-72982-2_6
Conference
Allocating Services to Applications using Markov Decision ProcessesIEEE International Conference on Service-Oriented Computing and Applications (SOCA '07)10.1109/SOCA.2007.7
Chapter
Dynamic Web Service Composition Using AI Planning Technique: Case Study on Blackbox PlannerSocial Networking and Computational Intelligence10.1007/978-981-15-2071-6_15
Chapter
Optimal Admission Control for a QoS-Aware Service-Oriented SystemTowards a Service-Based Internet10.1007/978-3-642-24755-2_17
Chapter
Traffic Management for Cloud FederationAutonomous Control for a Reliable Internet of Services10.1007/978-3-319-90415-3_11
Article
A hierarchical framework for logical composition of web servicesService Oriented Computing and Applications10.1007/s11761-009-0052-9
Article
Indeterminacy-aware service selection for reliable service compositionFrontiers of Computer Science in China10.1007/s11704-010-0077-4
Conference
Selective Querying for Adapting Hierarchical Web Service Compositions Using Aggregate Volatility2009 IEEE International Conference on Web Services10.1109/ICWS.2009.112
Conference
Optimal Adaptation in Web Processes with Coordination Constraints2006 IEEE International Conference on Web Services (ICWS'06)10.1109/ICWS.2006.97
Chapter
A Constrained Object Model for Configuration Based Workflow CompositionBusiness Process Management Workshops10.1007/11678564_10
Chapter
Adaptive Service Composition Based on Reinforcement LearningService-Oriented Computing10.1007/978-3-642-17358-5_7
Conference
Dynamic Web Service Composition Based on State Space Searching2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS.2015.110
Article
AN APPROACH TO CONSTRUCTING HIGH-AVAILABLE DECENTRALIZED SYSTEMS VIA SELF-ADAPTIVE COMPONENTSInternational Journal of Software Engineering and Knowledge Engineering10.1142/S0218194009004295
Conference
A Multi-agent Reinforcement Learning Model for Service Composition2012 IEEE Ninth International Conference on Services Computing10.1109/SCC.2012.58
Conference
Dynamic Adaption in Composite Web Services Using Expiration Times2009 International Conference on Computer Engineering and Technology10.1109/ICCET.2009.146
Chapter
CompositionSemantic Web Services10.1007/3-540-70894-4_9
Conference
Research on the Web Service Composition and Application Based on the Service Quality2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics10.1109/IHMSC.2015.144
Article
Efficient dynamic resource allocation method for cloud computing environmentCluster Computing10.1007/s10586-020-03053-x
Conference
Improved Adaptation of Web Service Compositions Using Value of Changed InformationIEEE International Conference on Web Services (ICWS 2007)10.1109/ICWS.2007.104
Article
Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization ApproachElectronics10.3390/electronics9061010
Conference
Towards an automatic approach for ubiquitous robotic services composition2008 IEEE/RSJ International Conference on Intelligent Robots and Systems10.1109/IROS.2008.4650981
Article
A Reinforcement Learning Method for Constraint-Satisfied Services CompositionIEEE Transactions on Services Computing10.1109/TSC.2017.2727050
Article
Dynamic Service Selection at Runtime for Service Supply ChainIFAC Proceedings Volumes10.3182/20130619-3-RU-3018.00050
Chapter
Adaptive Web Processes Using Value of Changed InformationService-Oriented Computing – ICSOC 200710.1007/11948148_15
Chapter
Risk Sensitive Value of Changed Information for Selective Querying of Web ServicesService-Oriented Computing10.1007/978-3-642-17358-5_6
Article
Improving POMDP Tractability via Belief Compression and ClusteringIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)10.1109/TSMCB.2009.2021573
Article
A parallel framework for Bayesian reinforcement learningConnection Science10.1080/09540091.2014.885268
Conference
Implementation Issues for Automatic Composition of Web Services2010 Workshops on Database and Expert Systems Applications10.1109/DEXA.2010.54
Article
Semantic web services: Standards, applications, challenges and solutionsJournal of Network and Computer Applications10.1016/j.jnca.2014.04.015
Chapter
Towards Proactive Web Service AdaptationActive Flow and Combustion Control 201810.1007/978-3-642-31095-9_31
Conference
A Learning Architecture for Scheduling Workflow Applications in the Cloud2011 IEEE Ninth European Conference on Web Services10.1109/ECOWS.2011.27
Conference
Real-Time QoS Control for Service Orchestration2015 27th International Teletraffic Congress10.1109/ITC.2015.25
Conference
Haley: A Hierarchical Framework for Logical Composition ofWeb ServicesIEEE International Conference on Web Services (ICWS 2007)10.1109/ICWS.2007.95
Conference
Web Based Composition using Machine Learning Approaches: A Literature ReviewProceedings of the 4th International Conference on Networking, Information Systems & Security10.1145/3454127.3457623
Article
Process parameter predictionviamarkov models of sub-activitiesRAIRO - Operations Research10.1051/ro/2014009
Article
An Intelligent Web Service Composition and Resource-Optimization Method Using K-Means Clustering and Knapsack AlgorithmsMathematics10.3390/math9172023
Chapter
Autonomous HVAC Control, A Reinforcement Learning ApproachMachine Learning and Knowledge Discovery in Databases10.1007/978-3-319-23461-8_1
Conference
Dynamic Web Service Evaluation Using Markov Process2008 4th International Conference on Wireless Communications, Networking and Mobile Computing10.1109/WiCom.2008.1864
Conference
A Dynamic Generation Algorithm for Meta Process Using Markov Decision ProcessesFirst International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06)10.1109/IMSCCS.2006.5
Conference
Configuration and Optimization of Virtual Business in Cloud Computing Environment2012 Second International Conference on Cloud and Green Computing10.1109/CGC.2012.73
Conference
QoS based framework for ubiquitous robotic services composition2009 IEEE/RSJ International Conference on Intelligent Robots and Systems10.1109/IROS.2009.5354328
Chapter
A Novel Fault-Tolerant Approach to Web Service Composition upon the Edge Computing EnvironmentWeb Services – ICWS 202110.1007/978-3-030-96140-4_2
Conference
Revenue maximization with quality assurance for composite web services2012 Fifth IEEE International Conference on Service-Oriented Computing and Applications (SOCA)10.1109/SOCA.2012.6449452
Conference
Web service recommendation based on QoS prediction method9th IEEE International Conference on Cognitive Informatics (ICCI'10)10.1109/COGINF.2010.5599757
Conference
Configuration based workflow compositionIEEE International Conference on Web Services (ICWS'05)10.1109/ICWS.2005.38
Chapter
Easy Flow: New Generation Business Process ModelAdvances in Web and Network Technologies, and Information Management10.1007/978-3-642-03996-6_14
Conference
Optimization of Web Service Composition Using Factored Markov Decision Process2009 Second International Workshop on Computer Science and Engineering10.1109/WCSE.2009.629
Conference
Integration of programming and decision theoretic planning for Web services composition2008 Chinese Control and Decision Conference10.1109/CCDC.2008.4598257
Conference
Speeding up web service composition with volatile external informationProceedings of the 2008 international workshop on Context enabled source and service selection, integration and adaptation: organized with the 17th International World Wide Web Conference (WWW 2008)10.1145/1361482.1361486
Conference
Learning Recommendation System for Automated Service Composition2013 IEEE International Conference on Services Computing10.1109/SCC.2013.66
Conference
Automatic service composition using POMDP and provenance data2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)10.1109/CIDM.2013.6597243
Conference
The design of a self-healing composition cycle for Web services2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems10.1109/SEAMS.2009.5069070
Conference
Context-Aware Dynamic Service Composition in Ubiquitous Environment2010 IEEE International Conference on Communications10.1109/ICC.2010.5502384
Article
Selective Querying for Adapting Web Service Compositions Using the Value of Changed InformationIEEE Transactions on Services Computing10.1109/TSC.2008.11
Chapter
Selective Querying for Adapting Hierarchical Web Service CompositionsEngineering Reliable Service Oriented Architecture10.4018/978-1-60960-493-6.ch007
Article
Using Markov Decision Process Model with Logic Scoring of Preference Model to Optimize HTN Web Services CompositionInternational Journal of Web Services Research10.4018/jwsr.2011040103

Dynamic Workflow Composition: Using Markov Decision Processes

Prashant Doshi, Richard Goodwin, Rama Akkiraju, Kunal Verma

Source Title: International Journal of Web Services Research (IJWSR)2(1)

Cite Article Cite Article

MLA

Doshi, Prashant, et al. "Dynamic Workflow Composition: Using Markov Decision Processes." IJWSR vol.2, no.1 2005: pp.1-17. http://doi.org/10.4018/jwsr.2005010101

APA

Doshi, P., Goodwin, R., Akkiraju, R., & Verma, K. (2005). Dynamic Workflow Composition: Using Markov Decision Processes. International Journal of Web Services Research (IJWSR), 2(1), 1-17. http://doi.org/10.4018/jwsr.2005010101

Chicago

Doshi, Prashant, et al. "Dynamic Workflow Composition: Using Markov Decision Processes," International Journal of Web Services Research (IJWSR) 2, no.1: 1-17. http://doi.org/10.4018/jwsr.2005010101

Export Reference

Favorite Full-Issue Download

View Full Text PDF

Abstract

The advent of Web services has made automated workflow composition relevant to Web-based applications. One technique that has received some attention for automatically composing workflows is AI-based classical planning. However, workflows generated by classical planning algorithms suffer from the paradoxical assumption of deterministic behavior of Web services, then requiring the additional overhead of execution monitoring to recover from unexpected behavior of services due to service failures, and the dynamic nature of real-world environments. To address these concerns, we propose using Markov decision processes (MDPs) to model workflow composition. To account for the uncertainty over the true environmental model, and for dynamic environments, we interleave MDP-based workflow generation and Bayesian model learning. Consequently, our method models both the inherent stochastic nature of Web services and the dynamic nature of the environment. Our algorithm produces workflows that are robust to non-deterministic behaviors of Web services and that adapt to a changing environment. We use a supply chain scenario to demonstrate our method and provide empirical results.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Dynamic Workflow Composition: Using Markov Decision Processes

MLA

APA

Chicago

Export Reference

Abstract

Request Access