Web Application Based on Reinforcement Learning - Codeshoppy

 Web Application Based on Reinforcement Learning - Codeshoppy

 In Web service enabled networks, typically a client programfirst locates a Web service server that can satisfy certainrequests from a yellow page (UDDI) and obtain a detailedspecification (WSDL) about the service. Then, using theknown API in the specification, the client sends a request tothe Web service considered via a standard messageprotocol (SOAP), and in return, it receives a response fromthe service. Web services are self-explanatory; by interpret-ing XML tags, applications can understand the semantics ofoperations. In particular, a problem of practical interestconcerns the following two issues. Given a requestr,among thousands of candidate Web services found inUDDI: 1) how we can find matching services that satisfyrand 2) how we can compose multiple services to satisfyrwhen a matching service does not exist. Consider the fourWeb services in Table 1, as illustrated in WSDL notation:

To fulfillr1, invoking the Web service issufficient. That is, by invokingfindHotel(“Atherton,”“State College,” “PA”), one can get the address of the hotelas “100 Atherton Street” with the zip code of “16801.”However, none of the four Web services can satisfyr2alone.  hotelbut cannot provide a driving direction. On the other hand,the Web service find Direction can give a drivingdirection from one location to another but cannot locateany restaurant. Therefore, one has to use a chain of Webservices to fully satisfyr2. There are two possible methodsto carry out this task.  

 

Most of trustworthy web service selection simply focus on individual reputation and ignore the collaboration reputation between services. To enhance the collaboration trust during web service selection, a reputation model called collaboration reputation is proposed. The reputation model is built on web service collaboration network(WSCN), which is constructed in terms of the composite service execution log. Thus, the WSCN aims to maintain the trustworthy collaboration alliance among web services, In WSCN, the collaboration reputation can be assessed by two metrics, one called invoking reputation is computed by recommendation, which is selected from the community structure hiding in WSCN, the other is assessed by the invoked web service. In addition, the web service selection based on WSCN is designed.

 

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