  In this paper, we consider an online stochastic resource allocation problem
which takes a linear program as its underlying form. We analyze an adaptive
allocation algorithm and derives a constant regret bound that is not dependent
on the number of time periods (number of decision variables) under the
condition that the objective coefficient of the linear program is linear in the
corresponding constraint coefficients. Furthermore, the constant regret bound
does not assume the knowledge of underlying distribution.
