Joint Communication and Computing Optimization for Hierarchical Machine Learning Tasks Distribution
Bo Fan Yang, Xuelin Cao, Xiangfang Li, Timothy S. Kroecker, Lijun Qian · 2019
In this paper, a joint latency and energy minimization problem is considered for hierarchical machine learning tasks distribution (HMLTD) with mobile edge computing (MEC). Firstly, we propose a MEC based HMLTD framework enabling mobile devices embedded with shallow neural network (SNN) model to offload latency-sensitive computing-intensive tasks to a nearby MEC server (MES), which has a more powerful deep neural network (DNN) model. Then, we formulate the offloading strategy as a piecewise convex optimization problem to minimize the weighted-sum of latency and energy. A closed-form solution of the optimal tasks partition strategy is derived analytically for different scenarios, and then an optimal partial offloading strategy (OPOS) is proposed. As proof of concept, some insights are gained to demonstrate the key parameters affecting the task partition strategy. Numerical results are given to illustrate that the proposed offloading scheme outperforms the baseline scheme.