  We consider the problem of intelligent and efficient task allocation
mechanism in large-scale mobile edge computing (MEC), which can reduce delay
and energy consumption in a parallel and distributed optimization. In this
paper, we study the joint optimization model to consider cooperative task
management mechanism among mobile terminals (MT), macro cell base station
(MBS), and multiple small cell base station (SBS) for large-scale MEC
applications. We propose a parallel multi-block Alternating Direction Method of
Multipliers (ADMM) based method to model both requirements of low delay and low
energy consumption in the MEC system which formulates the task allocation under
those requirements as a nonlinear 0-1 integer programming problem. To solve the
optimization problem, we develop an efficient combination of conjugate
gradient, Newton and linear search techniques based algorithm with Logarithmic
Smoothing (for global variables updating) and the Cyclic Block coordinate
Gradient Projection (CBGP, for local variables updating) methods, which can
guarantee convergence and reduce computational complexity with a good
scalability. Numerical results demonstrate the effectiveness of the proposed
mechanism and it can effectively reduce delay and energy consumption for a
large-scale MEC system.
