Due to technological advancement, cloud computing is an inevitable form of computing these days and is considered a boon to mid-scale industries. As the usage of cloud computing increases day-by-day, the service deployment improves every single day, which paves the way for security threats as well. Finding trustworthy service is a highly challenging problem, which may lead to time consumption or end with inappropriate services. Due to this problem, end user needs trust based appropriate service with minimum time consumption and the service should be reliable too. Hence, a cloud service recommendation system is the current need of the cloud environment. From a pool of available cloud services, the proposed system can recommend the time conserving reliable trustworthy services. This work attempts to keep this as the goal and presents a cloud service recommendation system using clustering based trust degree computation algorithm. Trust measures are deliberating to compute the trust degree (TD) for each dynamic service, which is computed for every time and the historical information is maintained as well. Since the trust agent clustered the services in automated fashion, to isolates the most trustworthy services from all the available clustered cloud services and efficiently allocates services to the end user using trustworthy service allocation algorithm. Process of service search and recommendation needs minimum time consumption. Registering service with trust agent (TA) provides most reliable trust worthy services. The performance of this recommendation system is evaluated in terms of precision, recall, F-measure and time consumption rates. The average F-measure rate of the proposed work is computed by varying the count of users from 200 to 300 and the average F-measure rate is 91.85% with minimal time consumption than the existing approaches.

由于技术的进步，云计算已成为当今计算的必然形式，并且被认为是中型行业的福音。随着云计算的使用日益增加，服务部署每天都在改善，这也为安全威胁铺平了道路。寻找可信赖的服务是一个极富挑战性的问题，可能导致时间浪费或以不合适的服务告终。由于此问题，最终用户需要以最少的时间消耗基于信任的适当服务，并且该服务也应该可靠。因此，云服务推荐系统是云环境的当前需求。从一组可用的云服务中，建议的系统可以推荐节省可靠的可信赖服务的时间。这项工作试图将其作为目标，并提出了一种使用基于聚类的信任度计算算法的云服务推荐系统。会考虑使用信任措施来计算每个动态服务的信任度（TD），该信任度是每次计算的，并且还会维护历史信息。由于信任代理以自动化的方式对服务进行集群，因此可以将最可信赖的服务与所有可用的集群云服务隔离开来，并使用可信赖的服务分配算法将服务高效地分配给最终用户。服务搜索和推荐过程需要最少的时间消耗。向信任代理（TA）注册服务可提供最可靠的值得信任的服务。此推荐系统的性能是根据准确性，召回率，F度量和时间消耗率。通过将用户数量从200改变为300，可以计算出所建议作品的平均F测量率，并且平均F测量率为91.85％，与现有方法相比，其耗时最少。