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A dynamic file grouping strategy is presented to address the load balancing problem in streaming media clustered server systems. This strategy increases the server cluster availability by balancing the workloads among the servers within a cluster. Additionally, it improves the access hit ratio of cached files in delivery servers to alleviate the limitation of I/O bandwidth of storage node. First, the load balancing problem is formulated as a two layer semi-Markov switching state-space control process. Then, a gradient-based reinforcement learning algorithm is proposed to optimize the grouping policy online. This analytic model captures the behaviors of streaming media clustered server systems accurately, and is with constructional flexibility and scalability. By utilizing the features of the event-driven policy, the proposed optimization algorithm is adaptive and with less computational cost. Simulation results demonstrate the effectiveness of the proposed approach.