Joint Resource Allocation at Edge Cloud Based on Ant Colony Optimization and Genetic Algorithm

Both the radio resources in wireless networks and the computational resources in cloud have big impact on the performance of the mobile cloud computing system. In this paper, we study the joint radio and computational resource allocation in a mobile edge cloud system with a heterogeneous radio access network and a close-by edge cloud. The objective of the proposed resource allocation scheme is to maximize the system utility as well as satisfy the diverse quality requirements for the delay-sensitive and computation-intensive applications of mobile users. The requirements for economic cost reduction and energy conservation are considered in the proposed scheme to achieve the balance between the user-centric and network-centric resource allocation. The proposed scheme takes advantage of both ant colony optimization (ACO) and genetic algorithm (GA) to explore and exploit the search space to obtain the near optimal solution at the lower computational complexity. ACO is applied for generating the initial population, and GA operations such as mapping, crossover, and repair are proposed to improve the search ability and avoid premature convergence through the search of solution in a broader search space. Simulation results show that our proposed scheme outperforms the existing schemes in terms of convergence performance and the accuracy of final results. Moreover, the results demonstrate that it can not only achieve significant system utility improvement, but also achieve higher resource utilization as well as remarkably lower average latency.