Joint Task Secure Offloading and Resource Allocation for Multi-MEC Server to Improve User QoE

Abstract

As a new computing paradigm after cloud computing, Mobile-edge computing (MEC) sinks computing power to the edge of the network, provides data caching and processing functions, and has the characteristics of low latency, high security, and location awareness. Users offloaded computing-intensive tasks to edge servers with stronger processing capabilities to further satisfy their QoE. A joint task offloading and resource allocation strategy was proposed to maximize users’ offloading gain by weighting task execution time and energy consumption. The above optimization problem is modeled as a mixed-integer non-linear programming problem that jointly optimizes task offloading decisions, the mobile users’ uplink transmission power, and edge server computing resource allocation. In a large-scale communication network, it is difficult to optimize the above problems to achieve the optimal solution. Therefore, we decoupled the above problems into the resource allocation problem under the fixed offloading decision and the offloading decision problem under the optimal resource allocation. Simulation results showed that the proposed method could effectively meet the users’ QoE. As the bandwidth compression factor γ and transfer data ${d_{u}}$ increase, the system utility function decreases. Well, as task loading ${C_{u}}$ increases, the system utility function increases.