The process of the Service Requester consists of four phases: requirements specification, configuration, buying & using, rating.
The process starts with creating a new request. Service Requesters conduct a dialog with a chatbot, where they describe the functional and non-functional requirements of their tailor-made service composition. The chatbot extracts a machine-readable request specification from requester’s answers and sends them to the Market Provider. The Market Provider broadcasts the Service Requests to all OTF Providers via a self-stabilizing Publish-Subscribe system. The OTF Providers respond with a confidence score that estimates how well they can solve the problem. Based on this confidence score and the overall reputation of the OTF Providers, the Service Requester chooses one OTF Provider from whom he wants to receive offers.
The configuration process starts and the Service Requester can inspect the progress of the request. The OTF Provider uses templates for Machine Learning Pipelines and fills the placeholder of the templates with basic services by using a heuristic search. The configuration search space is visualized as a graph. Every node represents a concrete Machine Learning Pipeline.
Buying and Using
After the configuration process is done, the OTF Provider offers the Machine Learning Pipelines to the Service Requester. The offers differ in their non-functional properties and the Service Request chooses the offer that best fits his needs and buys the service composition. The composition is automatically deployed in the Computer Center, where it is ready to be used. An access control prevents that unauthorized third-parties can use the service composition.
Finally, Service Requesters can rate their service composition and share their experience with other Service Requesters.