Abstract
In this paper, the problem of joint user scheduling and computing resource
allocation in asynchronous mobile edge computing (MEC) networks is studied. In
such networks, edge devices will offload their computational tasks to an MEC
server, using the energy they harvest from this server. To get their tasks
processed on time using the harvested energy, edge devices will strategically
schedule their task offloading, and compete for the computational resource at
the MEC server. Then, the MEC server will execute these tasks asynchronously
based on the arrival of the tasks. This joint user scheduling, time and
computation resource allocation problem is posed as an optimization framework
whose goal is to find the optimal scheduling and allocation strategy that
minimizes the energy consumption of these mobile computing tasks. To solve this
mixed-integer non-linear programming problem, the general benders decomposition
method is adopted which decomposes the original problem into a primal problem
and a master problem. Specifically, the primal problem is related to
computation resource and time slot allocation, of which the optimal closed-form
solution is obtained. The master problem regarding discrete user scheduling
variables is constructed by adding optimality cuts or feasibility cuts
according to whether the primal problem is feasible, which is a standard
mixed-integer linear programming problem and can be efficiently solved. By
iteratively solving the primal problem and master problem, the optimal
scheduling and resource allocation scheme is obtained. Simulation results
demonstrate that the proposed asynchronous computing framework reduces 87.17%
energy consumption compared with conventional synchronous computing
counterpart.