A RealTime Task Offloading Strategy Based on Double Auction for Optimal Resource Allocation in Edge Computing Task offloading in edge computing becomes an effective method to extend the computation ability of user equipments (UEs), via migrating computationintensive applications from UEs to edge servers. However, not only localityaware resource allocation for UEs and various edge computing services providers (ESPs) but also network economics for profitdriven ESPs and UEs is still a big challenge in task offloading. In this paper, we propose an edge computing resource allocation model based on the continuouscycle double auction mechanism (RABDA). Considering the emergency of task offloaded, we also propose realtime offloading strategy (RTOS) to ensure tasks are processed efficiently. We use genetic algorithm to determine the winner ESPs which are responsible for providing computational resources to UEs, and verify the performance of our algorithm by contrast experiment. The simulation results show that our algorithm can improve satisfaction between UEs and ESPs, and it has higher resource utilization than the existing algorithm.

Genetic AlgorithmBased Optimization of Offloading and Resource Allocation in MobileEdge Computing Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge serversxe2x80x99 (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESsxe2x80x99 processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of usersxe2x80x99 completion times.