As noted above, in On-The-Fly Computing, we have to deal with comprehensive service specifications describing many different properties of a service or a request. However, we cannot expect customers or providers to provide such comprehensive specifications for several reasons: For example, customers are not always willing and able to specify their needs completely and precisely. Similarly, providers do not always provide complete specifications as they do not know all details (e.g., the exact performance of a service) or because they want to protect business interests. This leads to the presence of incomplete and imprecise service specifications.
There are already many service matching approaches described in literature, but they are not able to deal with incomplete or imprecise service specifications. The reason is that current matching approaches make unrealistic assumptions, for example, they assume that the specifications are always precise and complete. Thus, we developed a new concept to address this challenge: Fuzzy Matching Processes.
Using Fuzzy Matching, we are able to classify and quantify different occurrences of fuzziness in service specifications: requester-induced fuzziness, provider-induced fuzziness, algorithm-induced fuzziness, and transformation-induced fuzziness. Depending on the kind of specification, quantifications can be based on different methods, e.g., percentage fuzziness scores or fuzzy sets.
In order to be able to deal with many different service properties, (fuzzy) matching approaches are combined with each other. Thereby, they build comprehensive matching processes consisting of several matching steps. These matching processes are configurable as they allow different combinations of matching steps as well as further variations, e.g., different aggregation strategies for matching results. This configurability allows us to optimize matching to the specific characteristics of a certain market.