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 <inline-formula><tex-math notation="LaTeX">$T>0$</tex-math></inline-formula> rounds, the incentive to misreport information has a uniform bound that is <inline-formula><tex-math notation="LaTeX">$O(\frac{1}{\sqrt{T}})$</tex-math></inline-formula>. We illustrate the application of the proposed mechanism to optimize the efficiency in <italic>MHA networks</italic>, an emerging type of heterogeneous networks where users can simultaneously connect to several access technologies.