SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneous devices in SAGIN pose significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also threaten the QoS for users. In this regard, this paper proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a SDN, NFV, and MEC, aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize the operator deployment costs while ensuring the QoS, this paper formulates a resource scheduling optimization model tailored to SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm, named DRL-G, which is based on heuristics and DRL, aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results showed that SAGIN-MEC can reduce the end-to-end delay by 6–15 ms compared to the terrestrial edge network, and compared to other algorithms, the DRL-G algorithm can improve the service request reception rate by up to 20%. In terms of energy consumption, it reduces the average energy consumption by 4.4% compared to the PG algorithm.

Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However, virtual network embedding (VNE) involving the allocation of resources for heterogeneous virtual network requests (VNRs) on the substrate network (SN) is considered as NP-hard problem. VNE process may involve conflicting objectives, including energy saving and VNR acceptance rate as the most critical. In this paper, we propose a virtual network multi-objective embedding algorithm based on Q-learning and curiosity-driven (Q-CD-VNE) for improving the performance of the system by optimizing conflicting objectives, namely energy saving and acceptance rate. The proposed algorithm employs Q-learning and curiosity-driven mechanism by considering other non-deterministic factors to avoid falling into a local optimum. The major contributions of this work involve (1) modeling of the multi-objective deterministic factors as binary (0, 1) integer programming problem, (2) formulating the virtual node mapping problem using the Markov decision process (MDP), (3) solving the VNE problem using Q-learning algorithm, (4) mining non-deterministic factors using curiosity-driven mechanism for avoiding prematurely falling into the Exploration-Exploitation dilemma and local optimal. Experimental results in comparison with representative researches in the field prove that the proposed algorithm can reduce energy consumption, improve the request acceptance rate, and improve the long-term average income.

One of the core challenges facing network virtualization is how to manage the underlying resources to host more virtual networks (VNs) with less energy. With the goal of reducing the energy consumption of virtual network embedding (VNE) and ensuring the VNE solution validity, this paper presents a new approach based on the bacterial foraging optimization (BFO) algorithm devoted to using the underlying resources more effectively and reasonably. In particular, by mimicking the information interaction between bacteria in the process of foraging, we devise a topology-aware mapping strategy, known as SBR_E, to build the candidate set of the VNE solutions. Then, we leverage SBR_E to design the energy manager of the optimization process and develop an energy-driven VNE algorithm based on BFO identified as EBFO to search for the optimal solution. Experimental results show that our method is superior to the existing algorithms in terms of reducing energy consumption and improving the VN request acceptance rate and revenue-cost ratio.

To bring 5G systems and networks to life in large-scale commercial applications, academia community has started the research beyond 5G (B5G), in which Network Slicing (NS) is proposed as a new paradigm for building service-tailored B5G networks. In each network slice, to precisely control the service quality and cost, deploying the service-required virtual network functions (VNFs) by utilizing the linkage between the characteristics of this slicing task and the characteristics of different servers in the B5G network is essential. Therefore, aiming at gaining the ability of learning and adapting new tasks quickly and cost-effectively, we view the NFV deployment problem as a meta relational learning process that explores the meta mapping relation between service-tailored slicing tasks and the B5G physical network, and propose a service-tailored VNF deployment framework, abbreviated as StailNet. Instead of training a one-strategy-fits-all deployment model, we focus on “learning” how to train a deployment model, and propose to learn the features of servers and slicing tasks from the perspective of knowledge graph-based representation learning, then locate the initial meta mapping relation by extracting meta information in the task-agnostic meta space and exploring the service-tailored meta mapping relation in the task space for each task, so that we can quickly obtain the solution by a few gradients on the initial meta mapping relation. To highlight the performances of StailNet, we do comprehensive simulations. Simulation results demonstrate that our StailNet outperforms the selected representative algorithms in the literature.