In the environment of data integration over the Internet, three major factors affect the cost of a query: network congestion situation, server contention states (workload), and data/query complexity. In this paper, we concentrate on system contention states. For a remote data source, we first determine the total number of contention states of the system through applying clustering techniques to the costs of sample queries. We then develop a set of cost formulae for each of the contention states using a multiple regression process. Finally, we estimate the system's current contention state when a query is issued and using either a time slides method or a statistical method depending on the information we have about the system. Our method can accurately predict the system contention state so that the effect of the contention states on the cost of queries can be estimated precisely.