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_15: The Cloud computing paradigm is adopted for its several advantages like reduction of cost incurred when using a set of resources. Despite the many proven benefits of using a Cloud infrastructure to run business processes, it is still faced with a major problem that can compromise its success: the lack of guidance for choosing between multiple offerings. To ensure this, we propose a set of algorithms for business process scheduling in Cloud computing environments. More precisely, we propose an extension of our previous approaches taking into account the fact that several instances of the same process can run simultaneously, and they may have to share the same resources. The proposed approaches take into account the Cloud elasticity feature on the one hand, and on the other hand, they consider the two most important quality of service criteria when running business process in Clouds environment, namely (i) the overall execution time and (ii) the cost incurred using a set of resources. In addition they allow to ensure fairness between the different concurrent business process instances.
Although significant attention has been received in service selection, few selection algorithms have been proposed when multiple service requests are in the service-oriented system for concurrent execution, no wonder from the point of fairness. Bi-criteria @cite_15 analyzed concurrent sequential workflows from two aspects which are makespan and execution cost, and brought forward a fairness-aware service selection policy, preventing the running service of workflow from prolonged idleness and starvation. Specifically, the less a running service is affected by others fighting for simultaneous execution with shared candidates, the higher priority it attains to be selected to be performed in advance. In this way, all of concurrent workflows are well-done to be executed without over penalized. Services with highest execution priority make up the Pareto Set, solved by heuristic algorithms.