Cloud computing markets face challenges, such as a large variety of different resource types in the market. A large variety of resource types results in a low number of matches of ask and bids. Consequently, the market has a low liquidity, which is economically inefficient and can lead to market failure. To counteract this problem, SLA templates (i.e., templates for electronic contracts) have been introduced. However, until now, SLA templates were static, not able to reflect changes in users'

more »

... quirements. In this paper, we apply the adaptive SLA mapping approach for deriving public SLA templates from private SLA templates of users. To achieve the adaptation of SLA templates, we combine clustering algorithms with adaptation methods. The result is a set of new public SLA templates, which reflect the market situation more accurately. This way, we enable marketplaces to automatically adapt to observed changes of market conditions. For the simulationbased evaluation of our approach, we formalize a utility and cost model, calculate the overall net utility, and assess the scalability of the approach. Our results show that the use of clustering algorithms can significantly improve the performance of the adaptive SLA mapping approach.