This paper concerns the problem of allocating network capacity through periodic auctions, in which users submit bids for fixed amounts of end-to-end service. We seek a distribute d allocation policy over a general network topology that optimizes revenue for the operator, under the provision that resources allocated in a given auction are reserved for the entire duration of the connection. We first study periodic auctions under reservations for a single resource, modeling the optimal revenue problem as a Markov Decision Process (MDP), and developing a receding horizon approximation to its solution. Next, we consider the distributed allocation of a single auction over a general network, writing it as an integer program and studying its convex relaxation, techniques of proximal optimization are applied to obtain a convergent algorithm. Combining the two approaches we formulate a receding horizon optimization of revenue over a general network topology, leading to a convex program with a distributed solution. The solution is also generalized to the multipath case, where many routes are available for each end-to-end service. A simulation framework is implemented to illustrate the performance of the proposal, and representative examples are shown