Distributed, Private, and Derandomized Allocation of Subsidized Goods

Efficient resource allocation is challenging when privacy of users is important. Distributed solution approaches have recently been used extensively to find a solution for such problems. In this work, we study the efficiency of distributed AIMD algorithm for allocation of subsidized goods. To this end, we assign each user a suitable utility function describing the amount of satisfaction that it has from allocated resource. We define the resource allocation as a \emph{total utilitarianism} problem that is an optimization problem of sum of users utility functions subjected to capacity constraint. Recently, a stochastic state-dependent variant of AIMD algorithm is used for allocation of common goods among users with strictly increasing and concave utility functions. We improve this algorithm to allocate subsidized goods to users with concave and nonmonotonous utility functions as well as users with quasi-concave utility functions. We also derandomize the AIMD algorithm and compare its efficiency with the stochastic version. We then model resource allocation problem as a competition game to evaluate the efficiency properties of unique equilibrium when network parameters change. To illustrate the effectiveness of the proposed solutions, we present simulation results for a public renewable-energy powered charging station in which the electric vehicles (EV) compete to be recharged