  Multi-access edge computing (MEC) is a promising architecture to provide
low-latency applications for future Internet of Things (IoT)-based network
systems. Together with the increasing scholarly attention on task offloading,
the problem of edge servers' resource allocation has been widely studied. Most
of previous works focus on a single edge server (ES) serving multiple terminal
entities (TEs), which restricts their access to sufficient resources. In this
paper, we consider a MEC resource transaction market with multiple ESs and
multiple TEs, which are interdependent and mutually influence each other.
However, this many-to-many interaction requires resolving several problems,
including task allocation, TEs' selection on ESs and conflicting interests of
both parties. Game theory can be used as an effective tool to realize the
interests of two or more conflicting individuals in the trading market.
Therefore, we propose a bilateral game framework among multiple ESs and
multiple TEs by modeling the task outsourcing problem as two noncooperative
games: the supplier and customer side games. In the first game, the supply
function bidding mechanism is employed to model the ESs' profit maximization
problem. The ESs submit their bids to the scheduler, where the computing
service price is computed and sent to the TEs. While in the second game, TEs
determine the optimal demand profiles according to ESs' bids to maximize their
payoff. The existence and uniqueness of the Nash equilibrium in the
aforementioned games are proved. A distributed task outsourcing algorithm
(DTOA) is designed to determine the equilibrium. Simulation results have
demonstrated the superior performance of DTOA in increasing the ESs' profit and
TEs' payoff, as well as flattening the peak and off-peak load.
