Trustworthy Service Selection Integrating Cloud Model and Possibility Degree Ranking of Interval Numbers

Ma, Hua; Hu, Zhigang; Cai, Meiling

doi:10.1049/cje.2017.09.034

Cited by 5 publications

(1 citation statement)

References 14 publications

(22 reference statements)

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“…For example, the diversity of user demands and preferences should be carefully considered especially in the formulation of optimization objectives and constraints [16]. Trustworthiness should be formulated and optimized for better meeting the QoS requirements of the users [17]. Sometimes, the service selection can be formulated as a multidimensional, multiobjective, and multichoice problem, which is so complex to be NP-Hard and can only be solved approximately by heuristic algorithms [18].…”

Section: Related Workmentioning

confidence: 99%

A Simulation‐Based Approach of QoS‐Aware Service Selection in Mobile Edge Computing

Huang

Lan

2018

Wireless Communications and Mobile Computing

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Edge computing is an emerging computational model that enables efficient offloading of service requests to edge servers. By leveraging the well-developed technologies of cloud computing, the computing capabilities of mobile devices can be significantly enhanced in edge computing paradigm. However, upon the arrival of user requests, whether to dispatch them to the edge servers or cloud servers in order to guarantee the quality of service (QoS), i.e., the QoS-aware service selection problem, still remains an open problem. Due to the dynamic mobility of users and the variation of task arrivals and service processes, it is extremely costly to obtain the global optimal solution by both mathematical approaches and simulation-based schemes. To attack this challenge, this paper proposes a simulation-based approach of QoS-aware dynamic service selection for mobile edge computing systems. Stochastic system models are presented and mathematical analyses are provided. Based on the analytical results, the QoS-aware service selection problem is formulated by a dynamic optimization problem. Goal softening is applied to the original problem, and service selection algorithms are designed using ordinal optimization techniques. Simulation experiments are conducted to validate the efficacy of the approach presented in this paper.

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Section: Related Workmentioning

confidence: 99%