摘要
为解决云服务选择过程中的局部极值化问题,利用逼近于理想解的排序技术(TOPSIS),设计一种云服务选择算法。采用熵赋值法简化决策准则的权重选取,基于可用云服务对各时段内的QoS特征构建决策矩阵,并通过模糊TOPSIS等级选取和时变权重获得较优质的云服务进行融合决策,实现云服务的合理选择。仿真实验结果表明,该算法在云服务选择成功率和鲁棒性方面均优于对比算法,能有效遏制不良QoS数据干扰,提高诚信服务的共享性。
In order to solve the local extremum problem in cloud service selection process, this paper designs a cloud service selection algorithm based on Technique for Order Preference by Similarity to an Ideal Solution （TOPSIS）. The entropy value assignment method is used to simplify the criteria weights selection, and then decision matrix for each period of QoS characteristics is built based on the available cloud service, and fuzzy TOPSIS rank selection and timevarying weight are made fusion decision to obtain a high quality of cloud services,by which a reasonable choice of cloud service is realized. Simulation experimental results show that the proposed algorithm is better than contrast algorithm in success rate and robustness of cloud service selection,it can effectively curb the bad QoS data interference, and strengthen well faith service sharing.
出处
《计算机工程》
CAS
CSCD
北大核心
2015年第7期43-47,54,共6页
Computer Engineering
基金
四川省教育厅自然科学基金资助重点项目(13ZA0013)
关键词
逼近于理想解的排序技术
模糊时变权重
云服务
熵赋值法
融合决策
Technique for Order Preference by Similarity to an Ideal Solution（TOPSIS）
fuzzy time-variable weight
cloud service
entropy assignment method
fusion decision making
