An Online Mechanism for Resource Allocation in Networks

We consider the problem of designing an online mechanism for trading divisible resources over a constrained network. In an online setting, participating agents learn about their marginal valuations gradually over time. We propose a mechanism that implements the efficient allocation of resources with a finite-time guarantee, i.e., upon stopping after T > 0 rounds, the incentive to misreport information has a uniform bound that is O(1/√T). We illustrate the application of the proposed mechanism to optimize the efficiency in MHA networks, an emerging type of heterogeneous networks where users can simultaneously connect to several access technologies.