User-Centric Computation Offloading for Edge Computing

The number of smart devices newly connected to the Internet has grown exponentially in recent years. These smart devices are interwoven into huge Internet of Things. There is a contradiction between mass data transmission and communication bandwidth, the distance between supercomputing power and processing object, and the demand of frequent interaction and real-time response. As a new computing paradigm, edge computing processes tasks on computing resources close to data sources. Considering the limited energy of the mobile terminal and the user's demand for low delay, making decisions about tasks executed locally and offloaded to edge computing servers. In the edge environment, resources are dynamically allocated to users on demand, and users need to pay for the resources they actually consume. By considering energy consumption, delay, and price, a user-centered joint optimization loading scheme is proposed to minimize the weighted cost of time delay, energy consumption, and price under the constraint of satisfying the advanced personalized needs of users. The optimization problem is modeled as a mixed-integer nonlinear programming problem, and a branch-and-bound algorithm based on linear relaxation improvement is proposed to solve the problem. Considering the complexity of the algorithm, a particle swarm optimization algorithm based on 0-1 and weight improvement is proposed to solve the problem. Simulation results show that the method proposed in this article can achieve higher performance in terms of delay, energy consumption, and price and provide personalized service for users.