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.

CrossServer Computation Offloading for MultiTask Mobile Edge Computing As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computationintensive applications and the resourceconstrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the crossserver computation offloading problem to realize the collaboration among multiple edge servers for multitask mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give nearoptimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.