Multi-attribute optimization in service selection

As multiple service providers may compete to offer the same functionality with different quality of service (e.g., latency, fee, and reputation), a key issue in service computing is selecting service providers with the best user desired quality. Existing service selection approaches mostly rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and sometimes conflicting) quality attributes as numeric weights. This is a rather demanding task and an imprecise specification of the weights could miss user desired services. We propose a multi-attribute optimization approach to tackle this issue. In particular, we develop a novel concept, called service skyline, and a set of service skyline computation techniques that return a set of most interesting service providers. These providers are non-dominant in all user interested quality attributes. Thus, the service skyline ensures that the user desired providers will be included. Analytical and experimental studies justify the performance of the proposed techniques. The relative small sizes of the service skylines also make it practical for service users to make selections from them.