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When applied in high-churn Internet environments, P2P systems face a dilemma: although most participants are too unstable, a P2P system requires sufficient stable peers to provide satisfactory core services. Thus, determining how to leverage unstable nodes seems to be the only choice. Our primary idea is to group unstable nodes together in order to form an adequate number of stable service groups. Focusing on this topic, our main findings are three-fold: 1) A general analytical model to investigate the grouping process of P2P systems is established, in which the stability-scalability trade-off problem is paid special attention to. 2) We formalize the target of grouping as the Maximum Stability Grouping (MSG) problem. It proves to be not only NP-hard, but also infeasible; therefore, we restrict it to a feasible Homogeneous MSG (H-MSG) problem and deduce its optimal solution under the stochastic model. 3) We propose a homogeneous grouping strategy to fulfill the optimal solution. Comprehensive simulations have been performed on generated data sets and real-world traces from a P2P storage system and a P2P streaming system. Results show that our grouping strategy effectively captures the stability-scalability trade-off: besides excellent stability, it gains much higher stable service capacity, with acceptable loss in scalability.