  In mobile edge computing (MEC) systems, users offload computationally
intensive tasks to edge servers at base stations. However, with unequal demand
across the network, there might be excess demand at some locations and
underutilized resources at other locations. To address such load-unbalanced
problem in MEC systems, in this paper we propose virtual machines (VMs) sharing
across base stations. Specifically, we consider the joint VM placement and
pricing problem across base stations to match demand and supply and maximize
revenue at the network level. To make this problem tractable, we decompose it
into master and slave problems. For the placement master problem, we propose a
Markov approximation algorithm MAP on the design of a continuous time Markov
chain. As for the pricing slave problem, we propose OPA - an optimal VM pricing
auction, where all users are truthful. Furthermore, given users' potential
untruthful behaviors, we propose an incentive compatible auction iCAT along
with a partitioning mechanism PUFF, for which we prove incentive compatibility
and revenue guarantees. Finally, we combine MAP and OPA or PUFF to solve the
original problem, and analyze the optimality gap. Simulation results show that
collaborative base stations increases revenue by up to 50%.
