Evaluating Workflow Trust using Hidden Markov Modeling and Provenance Data

In service-oriented environments, services provide different qualities in terms of parameters like availability, cost, reputation, execution time, etc. A trust score can be derived from these QoS parameters, which determines the rate of reliability in each service. This score can assist the service consumer parties to decide whether or not to transact with that service provider in the future. In such distributed environments, services with different functionalities are combined together to define new services or provide higher level functionalities. Having a trust score for each service, the trust level of a combination of services, i.e. a workflow, can be determined. Assessing the trust value of a workflow helps to determine its rate of reliability. Therefore, the trustworthiness of the results of a workflow will be inferred to decide whether the workflow’s trust rate should be improved. The improvement can be done by replacing services with low trust levels with services with higher trust levels. In this paper, we provide a new approach for evaluating workflow trust based on the Hidden Markov Model (HMM). We first present how the workflow trust evaluation can be modelled as a HMM and provide information on how the model and its associated probabilities can be assessed. Then, we investigate the behaviour of our model by relaxing the stationary assumption of HMM and present another model based on non-stationary hidden Markov models. We compare the results of the two models and present our conclusions.