The Multi-access Edge Computing (MEC) system interconnected by metro optical network is gaining much attention recently. The proximity of computing resources in MEC provides the possibility of low-latency access for user equipment (UE). However, since the computing capacity of edge server is limited, the task failure may occur due to the unacceptable computing latency if the corresponding server is overloaded. Note that there is more than one MEC server at the network edge and the computationally intensive tasks on the overloaded server can be sent to nearby light-loaded servers for processing via peer offloading. In this article, we design a latency-aware task peer offloading (LA-TPO) scheme, which is an economic solution for the overload problem of MEC. We formulate the LA-TPO into a mixed integer non-linear programming (MINLP) model and develop a GA-based heuristic algorithm to jointly optimize the offloading decisions and the routing and computing resource allocation. We compare the MINLP and proposed algorithm under various scenarios, and the results show that the GA-based LA-TPO can achieve low latency, high success ratio of tasks as well as the optimization of resource utilization.

In recent years, hybrid fiber-wireless (Fi-Wi) networks that integrate optical network technology and wireless communication technology have received widespread attention. The technical integration in Fi-Wi networks provides users with the possibility to establish high-quality connections that guarantee flexibility and economy. However, there are heterogeneous structures and resource types between the subnets of the Fi-Wi network. This paper considers the segmented application of network slicing technology in the optical backbone and hybrid access parts of Fi-Wi networks. In this way, the seamless connection of wired and wireless networks can be realized, and meanwhile adapt to the multiple network service forms and differentiated service quality requirements introduced in the 5G network era. In this paper, we design a dynamic slicing (PDSM) scheme based on traffic prediction. This scheme includes all four procedures of service provisioning, which are network slice creation, traffic prediction, online reconfiguration of network slices and intra-slice routing and resource allocation based on the prediction results. It is more flexible than the traditional static slicing scheme in the scenario, where the network state changes dynamically. We develop a prediction-based dynamic slicing algorithm (PD-MSRA) with intra-slice multi-path routing and shared resource allocation for the entire workflow of PDSM. We evaluate the performance of PD-MSRA on a variety of network topologies, and also implement several comparison algorithms including static slicing strategies. Simulation results show that the PDSM scheme using PD-MSRA achieves low bandwidth blocking rate and the optimization of resource utilization.