There has been a recent unprecedented increase in the use of Online Social Networks (OSNs) to expand our social life, exchange information and share common interests. Many popular OSNs today attract hundreds of millions of users who share tremendous amount of data on it such as Facebook, Twitter, and Buzz. Given the huge business opportunities OSNs may bring, more and more new social applications has emerged on the Internet. For these newcomers in the social network business, one of the first

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... y decisions to make is to where to deploy the computational resources to best accommodate future client requests. In this work, we aim at providing useful suggests to the new born social network providers (freshman) on the intelligent server placement, by exploring available public information from existing social network communities. In this work, we first propose three scalable server placement strategies for OSNs. Our solution can scalably select server locations among all the possible locations, at the same time reducing the cost for inter-user data sharing.