Conditional Preference Networks for Cloud Service Selection and Ranking With Many Irrelevant Attributes

Cloud service selection and ranking are two different but related important tasks in the realm of cloud computing. Given the rapid growth in the number of available services offered by cloud providers with different quality of service, these tasks are becoming more and more challenging to be managed by the end customers. Furthermore, many services in the cloud are defined over a large set of attributes where the potential customer is interested on only a small subset of them. The other attributes are viewed as irrelevant to the customer selection and ranking strategies. In this work, we show how to employ conditional preferential dependencies in the domain of cloud service selection and ranking. In particular, we use Conditional Preference Networks (CP-nets) as a compact model to represent and reason with customer’s preferences and criteria interdependencies. We show how to select the best service and how to rank, in non-increasing order, a set of services given possibly incomplete information in the customer’s CP-net. Our experimental results prove the feasibility of CP-nets as a mechanism for selecting and ranking hundreds of services defined on hundreds of attributes with complex interdependencies.