Uncertain service selection using hesitant fuzzy sets and grey wolf optimisation

Dealing with complex user requirements and the exponential growth of cloud service providers in a dynamic environment is a challenging task. In practice, the quality of service (QoS) constitutes a crucial feature in cloud service selection (CSS). We generally assume that the QoS of cloud services is inherently dynamic, and consequently, CSS can be viewed as a complex multi-criteria decision-making (MCDM) issue. In this paper, we propose a grey wolf-based (GW) approach that uses entropy, cross-entropy and hesitant fuzzy sets to select the near-optimal cloud service compositions that best meet the user's need. According to the experiments, the proposed approach is very promising in terms of the computation time and the degree of satisfaction of global constraints.