A MECbased Distributed Offloading Model for Ubiquitous and Timeconstraint Offloading The advancements in mobile hardware and network technologies facilitate the processing power, storage capability, and connection quality. Such developments enable sophistic functions, ubiquitous power and bandwidthhungry applications that fundamentally changes the individualxe2x80x99s lifestyle. Although Cloud Computing technologies have already been leveraged to coordinate with the capability and batteryconstraint mobile User Equipment (UE), the longdistance propagation delay downgrades the network QoS and user QoE. In this paper, we propose a queueingbased Mobile Edge Computing (MEC) model that concerns the offloading procedure, especially in the timeconstraint scenarios. A queueing model is proposed for the offloading process, considering the dynamic network queueing delay. A heuristic scheduling model is designed to maximize the offloading energy and execution efficiency. A regression prediction model is implemented to achieve dynamic resource allocation. In the experiment, the proposed model is compared to the recent studies, and the results indicate that the proposed model can outperform the current studies in terms of execution time and energy reservation.

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.