The increasing momentum of service-oriented architecture has led to the emergence of divergent delivered services, where service selection is meritedly required to obtain the target service fulfilling the requirements from both users and service providers. Despite many existing works have extensively handled the issue of service selection, it remains an open question in the case where requests from multiple users are performed simultaneously by a certain set of shared candidate services. Meanwhile, there exist some constraints enforced on the context of service selection, e.g. service placement location and contracts between users and service providers. In this paper, we focus on the QoS-aware service selection with constraints from a fairness aspect, with the objective of achieving max-min fairness across multiple service requests sharing candidate service sets. To be more specific, we study the problem of fairly selecting services from shared candidate sets while service providers are self-motivated to offer better services with higher QoS values. We formulate this problem as a lexicographical maximization problem, which is far from trivial to deal with practically due to its inherently multi-objective and discrete nature. A fairness-aware algorithm for concurrent service selection (FASS) is proposed, whose basic idea is to iteratively solve the single-objective subproblems by transforming them into linear programming problems. Experimental results based on real-world datasets also validate the effectiveness and practicality of our proposed approach.
@cite_18: In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory in which only an overall value on all the criteria is evaluated, some evaluate one criterion at a time in the order of their importance levels, while others count on the number of winning criteria. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this paper, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a machine learning algorithm to learn a personalized ranking model for individual users based on how they select services in the past. We have implemented and tested the proposed approach, and our experiment results show the effectiveness of the approach.
@cite_10: With the fast development of Web services in service-oriented systems, the requirement of efficient Quality of Service (QoS) evaluation methods becomes strong. However, many QoS values are unknown in reality. Therefore, it is necessary to predict the unknown QoS values of Web services based on the obtainable QoS values. Generally, the QoS values of similar users are employed to make predictions for the current user. However, the QoS values may be contributed from unreliable users, leading to inaccuracy of the prediction results. To address this problem, we present a highly credible approach, called reputation-based Matrix Factorization (RMF), for predicting the unknown Web service QoS values. RMF first calculates the reputation of each user based on their contributed QoS values to quantify the credibility of users, and then takes the users' reputation into consideration for achieving more accurate QoS prediction. Reputation-based matrix factorization is applicable to the prediction of QoS data in the presence of unreliable user-provided QoS values. Extensive experiments are conducted with real-world Web service QoS data sets, and the experimental results show that our proposed approach outperforms other existing approaches.
@cite_6: Service functionality can be provided by more than one service consumer. In order to choose the service which creates the most benefit before its consumption, a selection based on previous measurable experiences by other consumers is beneficial. In this paper, we present the results of our analysis of two machine learning approaches to predict the best service within this selection problem. The first approach focuses on classification, predicting the best performing service, while the second approach focuses on regression, predicting service performances which can then be used for the determination of the best candidate. We assessed and compared both approaches for service recommendation w.r.t. The performance gain when selecting the recommended instead of a random service. Our evaluation is based on data measured on real Web services as well as on simulated data. The latter is needed for a more profound analysis of the strengths and weaknesses of each approach. The simulated data has similar statistical properties as the data measured on real Web services. In the real-world case, regression achieved a response time gain of over 92 of the optimum and classification over 83 . In case of simulated data, we could achieve an overall gain of up to 95 using classification, while regression achieved 89 .
Furthermore, the machine learning (ML) methodology is also taken into use, intelligentizing the procedure of service selection @cite_6 . On the basis of the reputation-driven matrix factorization approach, @cite_10 conducted accurate QoS prediction on unknown web services, supporting the context of service selection. @cite_18 designed a ML-based service selection approach based on learning-to-rank algorithm, taking advantage of historical service-selection decisions outcomes and eventually delivering personalized service selection of each user.