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Multi-objective service composition model based on cost-effective optimization

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Abstract

The widespread application of cloud computing results in the exuberant growth of services with the same functionality. Quality of service (QoS) is mostly applied to represent nonfunctional properties of services, and has become an important basis for service selection. The object of most existing optimization methods is to maximize the QoS, which restricts the diversity of users’ requirements. In this paper, instead of optimization for the single object, we take maximization of QoS and minimization of cost as two objects, and a novel multi-objective service composition model based on cost-effective optimization is designed according to the complicated QoS requirements of users. Furthermore, to solve this complex optimization problem, the Elite-guided Multi-objective Artificial Bee Colony (EMOABC) algorithm is proposed from the addition of fast nondominated sorting method, population selection strategy, elite-guided discrete solution generation strategy and multi-objective fitness calculation method into the original ABC algorithm. The experiments on two datasets demonstrate that EMOABC has an advantage both on the quality of solution and efficiency as compared to other algorithms. Therefore, the proposed method can be better applicable to the cloud services selection and composition.

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Acknowledgments

This work is supported by the Scientific Research Foundation of Nanjing Institute of Technology of China under Grant No. YKJ201614 and the Youth Foundation of Nanjing Institute of Technology of China under Grant No. QKJA201603.

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Authors and Affiliations

  1. Department of Computer Engineering, Nanjing Institute of Technology, Nanjing, 211167, China

    Ying Huo, Peng Qiu, Jiyou Zhai, Dajuan Fan & Huanfeng Peng

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  1. Ying Huo
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  2. Peng Qiu
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  3. Jiyou Zhai
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  4. Dajuan Fan
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  5. Huanfeng Peng
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Correspondence to Ying Huo.

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Huo, Y., Qiu, P., Zhai, J. et al. Multi-objective service composition model based on cost-effective optimization. Appl Intell 48, 651–669 (2018). https://doi.org/10.1007/s10489-017-0996-y

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