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

Table 8

Our method.

Our method	Descriptions
Ease of use	The state data of this method are easy to obtain, the influencing factors need to be analyzed in detail, and the rationality of the state transition matrix needs to be verified.
Objectivity	The input data of this method are the current running state of the service, which can be obtained from log data or test data, with strong objectivity.
Versatility	This method is applicable to the evolution scenario with stable operation environment factors, and has low universality.
Functionality	This method has a single function and can only judge the relationship with the trustworthiness of a given service composition.
Costs	The cost of this method is low, but its time complexity is high.