Throughput Maximization of Offloading Tasks in Multi-Access Edge Computing Networks for High-Speed Railways

With the development of high-speed railways (HSRs), people's lives have become more convenient. At the same time, people are demanding more data services on trains in terms of wireless communications and mobile computation. Recently, Multi-access Edge Computing (MEC), a new computing paradigm, has the great potential to improve the user experience of computing services by placing the cloud infrastructures closer to end-users so that users can offload their computation-intensive and delay-sensitive tasks to the edge servers. In this paper, we concentrate on finding proper data routing paths for each offloaded task and each process result, with the objective of maximizing the network throughput by serving as many tasks as possible while meeting the MEC network resource constraints and the user demand constraints within a finite time horizon of equal time slots. It is challenging because of the unspecific data volume of an offloaded task and handovers of task offloading procedures that may frequently occur. Thus, we first investigate the impact of handovers in uplinks and downlinks. Then we propose a graph-based model to portray the resource availability in the network. Furthermore, a heuristic algorithm is devised, which finds a set of single-source-single-destination delay-constrained shortest paths in a series of auxiliary graphs. Finally, we perform an empirical evaluation of the proposed algorithm by simulations. The simulation results show that the proposed algorithm could achieve a higher network throughput than the other two benchmark heuristics.