Guest Editorial: A Review of Worst-Case Execution-TimeAnalysis
Real-Time Systems - Special issue on worst-case execution-time analysis
Simulation, verification and automated composition of web services
Proceedings of the 11th international conference on World Wide Web
Distributed and Parallel Databases
On Structured Workflow Modelling
CAiSE '00 Proceedings of the 12th International Conference on Advanced Information Systems Engineering
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
QoS Aggregation for Web Service Composition using Workflow Patterns
EDOC '04 Proceedings of the Enterprise Distributed Object Computing Conference, Eighth IEEE International
Constraint Driven Web Service Composition in METEOR-S
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Efficient algorithms for Web services selection with end-to-end QoS constraints
ACM Transactions on the Web (TWEB)
Speeding up adaptation of web service compositions using expiration times
Proceedings of the 16th international conference on World Wide Web
Nonparametric Quantile Estimation
The Journal of Machine Learning Research
A probabilistic approach to modeling and estimating the QoS of web-services-based workflows
Information Sciences: an International Journal
Consistency of kernel-based quantile regression
Applied Stochastic Models in Business and Industry
Investigating web services on the world wide web
Proceedings of the 17th international conference on World Wide Web
A Stochastic Programming Approach for QoS-Aware Service Composition
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Determining QoS of WS-BPEL Compositions
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
Probabilistic QoS and Soft Contracts for Transaction-Based Web Services Orchestrations
IEEE Transactions on Services Computing
Monitoring, Prediction and Prevention of SLA Violations in Composite Services
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
A MVC Framework for Policy-Based Adaptation of Workflow Processes: A Case Study on Confidentiality
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
QoS-Aware Service Composition: A Survey
ECOWS '10 Proceedings of the 2010 Eighth IEEE European Conference on Web Services
QoS-Aware composition of web services: an evaluation of selection algorithms
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
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Services offered in a commercial context are expected to deliver certain levels of quality, typically contracted in a service level agreement (SLA) between the service provider and consumer. To prevent monetary penalties and loss of reputation by violating SLAs, it is important that the service provider can accurately estimate the Quality of Service (QoS) of all its provided (composite) services. This paper proposes a technique for predicting whether the execution of a service composition will be compliant with service level objectives (SLOs). We make three main contributions. First, we propose a simulation technique based on Petri nets to generate composite time series using monitored QoS data of its elementary services. This techniques preserves time related information and takes mutual dependencies between participating services into account. Second, we propose a kernel-based quantile estimator with online adaptation of the constant offset to predict future QoS values. The kernel-based quantile estimator is a powerful non-linear black-box regressor that (i) solves a convex optimization problem, (ii) is robust, and (iii) is consistent to the Bayes risk under rather weak assumptions. The online adaption guarantees that under certain assumptions the number of times the predicted value is worse than the actual value converges to the quantile value specified in the SLO. Third, we introduce two performance indicators for comparing different QoS prediction algorithms. Our validation in the context of two case studies shows that the proposed algorithms outperform existing approaches by drastically reducing the violation frequency of the SLA while maximizing the usage of the candidate services.