Joint Communication and Computing Optimization for Hierarchical Machine Learning Tasks Distribution 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 latencysensitive computingintensive 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 weightedsum of latency and energy. A closedform 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.

LatencyMinimized and EnergyEfficient Online Task Offloading for Mobile Edge Computing with Stochastic Heterogeneous Tasks Mobile edge computing (MEC) is a promising paradigm to meet the low latency requirement of applications like virtualaugmented reality, which moves computation workloads from remote cloud to network edge. Task offloading is a crucial problem in a MEC system, which significantly concerns with the achieved latency of tasks and the energy consumption on edge nodes. Although many efforts have been paid on the problem, most of them ignore the hierarchical architecture of MEC and the heterogeneity of tasks generated by different applications. In this paper, we consider a threelayer MEC system with tasks of various types stochastically arrive in real time, in which each task can be executed on mobile smart devices, offloaded to the edge server, or offloaded to the remote cloud. The task offloading problem in such a MEC system with the objective to minimize the averaged latency over time is NPhard. To solve it, we employ Lyapunov optimization and duality theory to reformulate the problem and decompose it into a set of subproblems. Each subproblem can be distributedly solved by a mobile device or the edge server individually. Extensive simulations are also performed to verify the feasibility and superiority of our approach.