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_24: Due to the limited capabilities and resources, edge servers cannot meet the increasingly complex and diverse service requirements in mobile edge computing environments. In this circumstance, how to dispatch the component tasks of service requests to edge and cloud servers to reduce the time delay has become a crucial problem. Therefore, we focus on this problem and propose a heuristic algorithm called GAMEC (combined Genetic algorithm and simulated Annealing algorithm for service selection in Mobile Edge Computing systems). The simulated experiments have demonstrated the high effectiveness of the method.
@cite_9: This paper proposes a method for selecting optimal cloud resource configurations that satisfy hard and soft user constraints and multiple conflicting objectives. In the proposed method, feasible configurations generation and optimal configurations selection are carried out as two separate and independent processes that execute either sequentially or concurrently depending on the level of complexity of the optimization problem and the size of its solution space. The feasible configurations generation process utilizes an equivalent transformation-based constraint satisfaction method to generate the universe of feasible resource configurations that satisfy user requirements and constraints for a given cloud resource selection problem. In the optimal resource configurations selection process, nondominated sorting, reference points association, and niching elitism are performed to produce a user-specified number of diverse Pareto-optimal configurations from the feasible resource configurations universe. The results of application of the proposed method to (1) a three-tier web application and (2) a cloud-based workflow resource allocation scenario verify its efficacy.
@cite_3: Service Oriented Architectures allow service brokers to execute business processes composed of network-accessible loosely-coupled services offered by a multitude of service providers, at different Quality of Service (QoS) and cost levels. To optimize their revenue and the offered QoS level, service brokers need to solve the problem of finding the set of service providers that minimizes the total execution time of the business process subject to cost and execution time constraints. This optimization problem is clearly NP-hard. Optimized algorithms that find the optimal solution without having to explore the entire solution space have been proposed to solve problems of moderate size. A heuristic search of the sub-optimal solution scales to problems of large size and is appropriate for runtime service selection. This paper evaluates the performance of three heuristic service selection algorithms that are candidates for implementation in scalable service brokers. Our goal is to identify which algorithm provides the solution closest to the optimal and how many selections are evaluated to find the solution. The comparison is made over a wide range of parameters including the complexity of the business process topology and the the tightness of the QoS and cost constraints.
The heuristic-driven approach is usually applied to reduce the computational cost of finding the optimal service, whose weakness resides in simply providing a sub-optimal selection closer to the optimum one @cite_3 . Addressing the issue of service selection in mobile edge computing environment, @cite_24 put forward a selection scheme minimizing the response time, whose arithmetic design integrated genetic and simulated annealing algorithm. @cite_9 conducted research on service selection in the case study of cloud resource configurations, structuring the set of feasible configuration from which diverse Pareto-optimal configurations were selected.