There have been many proposals on how media-on-demand servers can effectively allow clients to share resources. In this paper, given a set of clients, we show how these clients may be partitioned into "sync-classes" sets of clients who can be serviced through allocation of a single set of resources. As a set of clients may be partitioned into sync-classes in many different ways, we show that a very large class of cost functions may be used to determine which partition to choose. We provide algorithms to compute such optimal splits. Our framework is very generic in the following ways: the system may plug-in any cost function whatsoever, as long as it satisfies four common-sense axioms that evaluate costs; and the system may evaluate the future anticipated requests of a user using any user model (e.g., a Markovian model) that has a specified I/O interface. Thus, a wide variety of predictive methods (of what the user will do) and a wide variety of costing methods may be used within our framework.
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