Cloud service recommendation system based on clustering trust measures in multi-cloud environment

Abstract

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.