基于不确定服务质量感知的云服务组合方法

针对不确定服务质量（QoS）感知的云服务组合优化问题的求解，提出一种不定长时间序列（ULST）模型和锦标赛策略的改进遗传算法（T-GA）。首先，基于用户对服务不同时间段的访问规律，将服务质量的长期变化构建为不定长时间序列模型，该模型能够准确地描述一段时间内用户对服务的真实QoS访问记录。其次，提出一种基于不确定QoS模型的改进遗传算法，该算法采用锦标赛选择策略代替基本遗传算法中的轮盘赌选择策略。最后，在真实数据上进行了大量实验，所提的不定长时间序列模型能够有效地解决不确定QoS感知云服务组合问题，而锦标赛策略的改进遗传算法在寻优结果和稳定性方面均优于基于精英选择策略的遗传算法（E-GA）算法，且运行速度提高近1倍，是可行、高效且稳定的算法。

Cloud service composition method based on uncertain QoS-aware ness

To solve the problem of uncertain Quality of Service (QoS)-aware cloud service composition optimization, an Uncertain-Long Time Series (ULST) model and Tournament strategy based Genetic Algorithm (T-GA) was proposed. Firstly, based on different access rules of users to services in different periods, the long-term change of QoS was modeled as an uncertain-long time series, which can accurately describe the users' actual QoS access record to service over a period of time. Secondly, an improved genetic algorithm based on uncertain QoS model was proposed, which used tournament strategy instead of basic roulette wheel selection strategy. Finally, a lot of experiments were carried out on real data. The uncertain-long time series model can effectively solve the problem of uncertain QoS-aware cloud service composition; the proposed T-GA is superior to the Genetic Algorithm based on Elite selection strategy (E-GA) in optimization results and stability, and the execution speed is improved by almost one time, which is a feasible, high efficient and stable algorithm.