Virtual worlds, such as Second Life, are computer-simulated spaces divided into multiple regions, in which each user controls an avatar to perform actions (such as run and fly) in order to interact with other users. From a set of publicly available Second Life traces [1], we find that Second Life incurs diverse traffic patterns in different regions and with different actions. We therefore propose region-and action-aware Second Life clients, which adapt to and take advantages of the diverse traffic patterns for user-specified optimization criterion. For example, to prolong battery life, a mobile Second Life client switches from high capacity WiFi network to energy efficient cellular network when a Second Life client incurs low bit rate traffic. To efficiently achieve this, we propose a parameterized traffic model to accurately predict Second Life traffic patterns. We systematically derive the traffic model parameters using public Second Life traces, and we validate the model accuracy using another set of real network traces. To the best of our knowledge, region-and action-aware Second Life clients have never been considered in the literature, while the proposed parameterized traffic model is of interest in its own right, for Second Life developers, researchers, and Internet Service Providers (ISPs).