  In this paper, we study a general online linear programming problem whose
formulation encompasses many practical dynamic resource allocation problems,
including internet advertising display applications, revenue management,
various routing, packing, and auction problems. We propose a model, which under
mild assumptions, allows us to design near-optimal learning-based online
algorithms that do not require the a priori knowledge about the total number of
online requests to come, a first of its kind. We then consider two variants of
the problem that relax the initial assumptions imposed on the proposed model.
