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We consider the problem of intelligent and efficient task allocationmechanism in large-scale mobile edge computing (MEC), which can reduce delayand energy consumption in a parallel and distributed optimization. In thispaper, we study the joint optimization model to consider cooperative taskmanagement mechanism among mobile terminals (MT), macro cell base station(MBS), and multiple small cell base station (SBS) for large-scale MECapplications. We propose a parallel multi-block Alternating Direction Method ofMultipliers (ADMM) based method to model both requirements of low delay and lowenergy consumption in the MEC system which formulates the task allocation underthose requirements as a nonlinear 0-1 integer programming problem. To solve theoptimization problem, we develop an efficient combination of conjugategradient, Newton and linear search techniques based algorithm with LogarithmicSmoothing (for global variables updating) and the Cyclic Block coordinateGradient Projection (CBGP, for local variables updating) methods, which canguarantee convergence and reduce computational complexity with a goodscalability. Numerical results demonstrate the effectiveness of the proposedmechanism and it can effectively reduce delay and energy consumption for alarge-scale MEC system.

A Parallel Optimal Task Allocation Mechanism for Large-Scale Mobile Edge Computing