Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. This paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.
翻译:多入网量计算(MEC)有助于部署具有严格QOS要求的关键应用程序,特别是长期性。本文件审议了联合规划边缘计算资源的可用性、移动网络和边缘计算资源的切片、不同交通类型走向不同切片的路径等问题。这些方面是相互交织的,必须一起解决,以便向所有移动用户提供所希望的QOS,而交通类型仍然控制着费用。我们将我们的问题发展成一个混合整数非线性程序(MINLP),我们定义了一种称为邻里勘探和序列固定(NESF)的超常性,以促进问题的解决。这一方法使网络操作者能够微调网络运作成本和用户经历的全部耐久性。我们评估了拟议模型的性能,并针对两种自然贪婪做法进行了超常处理。我们展示了所有考虑参数(viz.,不同类型的交通,可耐受受控的拖网、网络顶部和带宽、计算和连接能力)在接近确定型号的模型中的变化所产生的影响。甚至计算和网络资源配置的短期模型都展示了实现甚大规模的模型的模型和模型资源分配的结果。