Joint Optimization of Computation Offloading and Resource Allocation in C-RAN with Multi-Access Edge Computing using Evolutionary Algorithms
preprintposted on 11.04.2022, 14:17 by Sumit SinghSumit Singh
The Mobile Edge computing (now known as multi-access edge computing) has been widely recognized as a key enabler for new latency sensitive applications on resource constrained mobile devices. The objective to offload a computationally intensive task to a cloud server has been extensively researched over the years. These research endeavours have been in general aimed at reducing system's energy consumption and/or latency. In this paper, we attempt to examine how profitable computation offloading is from a service provider's perspective. The joint optimization of radio and computational resources along with offloading decisions result in a mixed integer non-linear optimization problem which belong to the class of NP hard problems. To counter this challenge, we decouple the offloading decision from the resource allocation problem. At first, an approximate optimal offloading decisions are found using evolutionary algorithms like genetic algorithm and binary particle swarm optimization algorithm where the objective function is approximately calculated using machine learning based model rather than actually solving the optimization problem. Then the combination of selected mobile terminals for offloading further goes as input to resource allocation optimization problem. According to the simulations performed, the proposed evolutionary algorithms outperform spectrum efficiency based offloading algorithm in terms of profitability and have relatively lower execution times. Also, the impact of resource availability on profitability of offloading has been examined.