SaaS Service Combinatorial Trustworthiness Measurement Method Based on Markov Theory and Cosine Similarity

With the rapid progress of information technology, cloud computing and cloud services are widely accepted and applied to all aspects of social life. In the cloud computing environment, SaaS (Software-as-a-Service) services have become the main form of software services. For SaaS services, evolutionary and iterative development methods have become the main methods of software system construction. For systems with high trustworthiness, the independent trustworthiness of each SaaS service has a great impact on the overall status. However, SaaS services with high independent trustworthiness do not always build highly trusted software systems. The combinatorial trustworthiness between SaaS services is as important as the independent trustworthiness of each SaaS service. This paper takes combinatorial trustworthiness between SaaS services as the research object. Combinatorial trustworthiness measurement method based on Markov and cosine similarity theory is proposed. The feasibility and effectiveness of the proposed method are verified through simulation experiments. Applicable scenarios, advantages, and disadvantages of the proposed method are shown through the comparison of different measurement methods. The proposed method provides theoretical and technical support for users to select SaaS services suitable for their application scenarios, build cloud service systems, and monitor the operation status of cloud service systems.