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.
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.
@cite_24 @cite_9 @cite_3
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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. Mixed-machine heterogeneous computing (HC) environments utilize a distributed suite of different high-performance machines, interconnected with high-speed links, to perform different computationally intensive applications that have diverse computational requirements. HC environments are well suited to meet the computational demands of large, diverse groups of tasks. The problem of optimally mapping (defined as matching and scheduling) these tasks onto the machines of a distributed HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original study of each heuristic. Therefore, a collection of 11 heuristics from the literature has been selected, adapted, implemented, and analyzed under one set of common assumptions. It is assumed that the heuristics derive a mapping statically (i.e., off-line). It is also assumed that a metatask (i.e., a set of independent, noncommunicating tasks) is being mapped and that the goal is to minimize the total execution time of the metatask. The 11 heuristics examined are Opportunistic Load Balancing, Minimum Execution Time, Minimum Completion Time, Min?min, Max?min, Duplex, Genetic Algorithm, Simulated Annealing, Genetic Simulated Annealing, Tabu, and A*. This study provides one even basis for comparison and insights into circumstances where one technique will out-perform another. The evaluation procedure is specified, the heuristics are defined, and then comparison results are discussed. It is shown that for the cases studied here, the relatively simple Min?min heuristic performs well in comparison to the other techniques. Service Oriented Architectures enable a multitude of service providers to provide loosely coupled and interoperable services at different Quality of Service and cost levels. This paper considers business processes composed of activities that are supported by service providers. The structure of a business process may be expressed by languages such as BPEL and allows for constructs such as sequence, switch, while, flow, and pick. This paper considers 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. The problem is clearly NP-hard. However, the paper presents an optimized algorithm that finds the optimal solution without having to explore the entire solution space. This algorithm can be used to find the optimal solution in problems of moderate size. A heuristic solution is also presented. Thorough experimental studies, based on random business processes, demonstrate that the heuristic algorithm was able to produce service provider allocations that result in execution times that are only a few percentage points (less than 2.5 ) worse than the allocations obtained by the optimal algorithm while examining a tiny fraction of the solution space (tens of points versus millions of points). 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.