QoS Composition and Analysis in Reconfigurable Web Services Choreographies

Kattepur, Ajay; Georgantas, Nikolaos; Issarny, Valérie

doi:10.1109/icws.2013.40

Cited by 22 publications

(10 citation statements)

References 24 publications

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“…In case this is not exposed, the worst case performance for each domain must be expected at design time. At runtime, if re-configuration [15] or replacement/late-binding occurs, changes that may be expected through varied interaction paradigms may be evaluated. For instance, reduced S may be traded off with improvements in δ.…”

Section: Results: Qos In Choreography Interactionsmentioning

confidence: 99%

“…While our previous work [15] studies the effect of choreography topology on the QoS, by fine-grained analysis of message interactions, we evaluate the performance of choreography participants in relation to heterogeneous paradigms. This methodology enables: 1) Design-time selection of interaction paradigms to match required functional and QoS goals, and 2) Runtime analysis of composed choreographies to prevent deterioration of end-to-end QoS of participants.…”

Section: Introductionmentioning

confidence: 99%

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QoS Analysis in Heterogeneous Choreography Interactions

Kattepur

Georgantas

Issarny

2013

Service-Oriented Computing

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Abstract. With an increasing number of services and devices interacting in a decentralized manner, choreographies are an active area of investigation. The heterogeneous nature of interacting systems leads to choreographies that may not only include conventional services, but also sensor-actuator networks, databases and service feeds. Their middleware behavior within choreographies is captured through abstract interaction paradigms such as client-service, publish-subscribe and tuple space. In this paper, we study these heterogeneous interaction paradigms, connected through an eXtensible Service Bus proposed in the CHOReOS project. As the functioning of such choreographies is dependent on the Quality of Service (QoS) performance of participating entities, an intricate analysis of interaction paradigms and their effect on QoS metrics is needed. We study the composition of QoS metrics in heterogeneous choreographies, and the subsequent tradeoffs. This produces interesting insights such as selection of a particular system and its middleware during design time or end-to-end QoS expectation/guarantees during runtime. Non-parametric hypothesis tests are applied to systems, where QoS dependent services may be replaced at runtime to prevent deterioration in performance.

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Section: Results: Qos In Choreography Interactionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QoS Analysis in Heterogeneous Choreography Interactions

Kattepur

Georgantas

Issarny

2013

Service-Oriented Computing

Self Cite

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“…Among all topics in service computing, QoS-aware service selection and service composition are studied in a large number of literatures [15], [16], [17], [18], whose goal is to decide which candidate services to be used as components in complex systems. However, most of the research work has a necessary precondition: QoS values of all candidate services for corresponding users are already known, which is always not satisfied in real-world cases.…”

Section: Related Workmentioning

confidence: 99%

Location-Based Hierarchical Matrix Factorization for Web Service Recommendation

Zhu

Zheng

et al. 2014

2014 IEEE International Conference on Web Services

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Web service recommendation is of great importance when users face a large number of functionally-equivalent candidate services. To recommend Web services that best fit a user's need, QoS values which characterize the non-functional properties of those candidate services are in demand. But in reality, the QoS information of Web service is not easy to obtain, because only limited historical invocation records exist. To tackle this challenge, in recent literature, a number of QoS prediction methods are proposed, but they still demonstrate disadvantages on prediction accuracy. In this paper, we design a location-based hierarchical matrix factorization (HMF) method to perform personalized QoS prediction, whereby effective service recommendation can be made. We cluster users and services into several user-service groups based on their location information, each of which contains a small set of users and services. To better characterize the QoS data, our HMF model is trained in a hierarchical way by using the global QoS matrix as well as several location-based local QoS matrices generated from userservice clusters. Then the missing QoS values can be predicted by compactly combining the results from local matrix factorization and global matrix factorization. Comprehensive experiments are conducted on a real-world Web service QoS dataset with 1,974,675 real Web service invocation records. The experimental results show that our HMF method achieves higher prediction accuracy than the state-of-the-art methods.

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“…Also, the qualities of the selected composite Web services were not significantly different from the optimal one. Kattepur, Georgantas and Issarny (2013) proposed goal programming approach to choose Pareto optimal solutions with respect to diverse QoS domains.…”

Section: Mixed Integer Linear Programmingmentioning

confidence: 99%