Rank Aggregation for QoS-Aware Web Service Selection and Composition

Hofreiter, Birgit; Marchand-Maillet, Stéphane

doi:10.1109/soca.2013.36

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…In the simulation, we will compare our method to RQSS [8], MDSC (Multidimension Service Composition) [28], and RASC (Rank Aggregation Service Composition) [32]. RQSS [8] helps user discover feasible web services based on functionalities and QoS requirements.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…MDSC [28] first normalizes those QoS parameters and then uses the improved Euclidean Distance to select the web service which meets the requirement (in the simulation, we suppose every part has the same weight). RASC [32] uses Rank Aggregation methods to solve the web service composition.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…Therefore, the reliability and availability of service are ranging from 0.95 to 0.99999. Those settings are reasonable for web services [32]. In addition, the reputation has range of [1,10] and the price has range of [1,100].…”

Section: Simulation Environmentmentioning

confidence: 99%

“…RASC has the largest value in ET, the reason is the calculations of the Kendall Tau Distance between the service request and the service candidates needs much time [32]. The execution time is a polynomial function to the number of service candidates.…”

Section: Execution Timementioning

confidence: 99%

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Location‐Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests

Liu

et al. 2019

Complexity

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Web service composition is widely used to extend the function of web services. Different users have different requirements of QoSs (Quality of Services) making them face many problems. The requirement of a special QoS may be a hard requirement or a soft requirement. The hard requirement refers to the QoS which must be satisfied to the user, and the soft one means that the requirement is flexible. This paper tries to solve the service composition problem when there are two kinds of requirements of QoSs. To satisfy various kinds of requirement of the QoS, we propose a composition method based on our proposed framework. We give an analysis from composition models of services and from related QoE (Quality of Experience) of web services. Then, we rank the service candidates and the service requests together. Based on the ranking, a heuristics is proposed for service selection and composition-GLLB (global largest number of service requests first, local best fit service candidate first), which uses “lost value” in the scheduling to denote the QoE. Comparisons are used to evaluate our method. Comparisons show that GLLB reduces the value of NUR (Number of Unfinished service Requests), FV (Failure Value), and AFV (Average Failure Value).

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Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Experiments Results and Discussionmentioning

confidence: 99%