EdgeCloud Collaborative Computation Offloading Model Based on Improved Partical Swarm Optimization in MEC In order to reduce the delay and energy consumption of mobile devices, a computational offload strategy is adopted in mobile edge computing (MEC). At present, most computation offloading strategies only consider two computing resources, mobile devices and MEC servers. However, the computing power of the cloud server is much larger than that of the MEC server. Tasks with high computational complexity still need to be handed over to the cloud server for processing. This paper proposes an edgecloud collaborative multitask computing unloading model that considers both latency and energy cost. Usually the model solving is transformed into a search solution in finite strategy space. In this paper, the nonlinear exponential inertia weight particle swarm optimization (PSO) algorithm is used to get solution. By dynamically adjusting the inertia weight, the algorithm can make up for the convergence premature defect of the standard particle swarm optimization algorithm, and effectively avoid falling into the local optimal solution. Simulation experiments show that the strategy obtained by the model has lower total cost compared with different computation offloading models and strategies.

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.