Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms.

With the emerge of the network function virtualization (NFV) and software-defined network (SDN), the SDN/NFV-enabled network has been recognized as one of the most promising technologies to efficiently achieve resource allocation for network service. By introducing the SDN/NFV technology, each service can be represented by a service function chain (SFC), which can deploy the virtualized network functions (VNFs) and chain them with corresponding flows allocation. Considering the dynamic and complex nature of mobile terminals in cloud networks, how to efficiently embedding SFCs remains as a challenging problem. However, the traditional methods (e.g., exact, heuristic, meta-heuristic, and game, etc.) are subjected to the complexity of cloud network scenarios with dynamic network states, high-speed computational requirements, and enormous service requests. Recent studies have shown that deep reinforcement learning (DRL) is a promising way to deal with the limitations of the traditional methods. However, DRL agent training easily suffers from the problem of slow convergence performance. In order to overcome this narrow, in this paper, we design a novel DRL framework based on the enhanced deep deterministic policy gradient (E-DDPG) for the efficient SFC embedding in the dynamic and complex cloud network scenarios. Simulation results validate the high efficiency of the proposed DRL framework as it not only converges faster than currently baseline algorithms, but also reduces the end-to-end delay down to at least 28.3% compared to the benchmarks. All our proposed algorithms and code are available at https://github.com/ jn-z/.

Network function virtualization (NFV) and mobile edge computing (MEC) have been introduced by Internet service providers (ISPs) to deal with various challenges, which hinder them from satisfying ambitious quality of experience demands of the Internet of Things (IoT) applications. In NFV/MEC-enabled IoT networks, any IoT service can be expressed as a service function chain (SFC) consisting of several strictly ordered Virtual Network Functions (VNFs), which can be geographically placed onto edge clouds close to IoT terminals. However, regarding large number of IoT terminals and constantly dynamics of IoT networks, determining the placement of VNFs and routing service paths that optimize the end-to-end delays in the hybrid edge clouds is a challenging problem. This problem is also called SFC dynamic orchestration problem (SFC-DOP). To address the SFC-DOP, we are motivated to creatively present an SFC dynamic orchestration framework for IoT deep reinforcement learning (DRL). Also, a DRL-based algorithm for SFC-DOP with the actor-critic and the deterministic policy gradient scheme is provided, which can efficiently deal with the SFC-DOP in IoT networks. The obtained experimental results show the outstanding performance of our proposal compared with the current benchmarks.

With the rapid development of emitter individual identification technology in cognitive radio networks, electromagnetic emitter individual target identification based on deep learning has received much attention. However, the confusion of unintentional features (i.e., individual fingerprint features) and modulation features resulting from the received signal might lead to low identification accuracy. In order to address this narrow, we propose an emitter individual identification network based on the competitive collaboration framework, called Specific Emitter Identification with Adaptive Decomposition and Extraction of individual fingerprint features (SEI-ADE), which can adaptively decompose and extract individual fingerprint features. Firstly, a signal adaptive decomposition network is proposed to distinguish the emitter signal and the interference signal by adopting the gradient inversion layer and the non-sequential characteristics of the signal. Then, in order to distinguish and extract corresponding features, the feature extractor and the training loss constraints are constructed for individual fingerprint feature signals, modulation signals, and external emitter interference signals, respectively. The proposed framework can continuously adjust the gradient loss, classification loss, and timing coding contrast loss, thus minimizing the entire training loss. For the separation of the modulation signal and individual fingerprint feature signal, the signal is transformed into the feature domain, and a mask prediction network is proposed to locate the domain of the individual fingerprint feature. The obtained experimental results show the outstanding performance of our proposal, compared with the current benchmarks. All our models and code are available at https://github.com/jn-z/SEI-ADE.

The unmanned aerial vehicles (UAVs) networks are very vulnerable to smart jammers that can choose their jamming strategy based on the ongoing channel state accordingly. Although reinforcement learning (RL) algorithms can give UAV networks the ability to make intelligent decisions, the high-dimensional state space makes it difficult for algorithms to converge quickly. This article proposes a knowledge-based RL method, which uses domain knowledge to compress the state space that the agent needs to explore and then improve the algorithm convergence speed. Specifically, we use the inertial law of the aircraft and the law of signal attenuation in free space to guide the highly efficient exploration of the UAVs in the state space. We incorporate the performance indicators of the receiver and the subjective value of the task into the design of the reward function, and build a virtual environment for pretraining to accelerate the convergence of anti-jamming decisions. In addition, the algorithm proposed is completely based on observable data, which is more realistic than those studies that assume the position or the channel strategy of the jammer. The simulation shows that the proposed algorithm can outperform the benchmarks of model-free RL algorithm in terms of converge speed and averaged reward.

Service function chain (SFC) has been recognized as one of the most important technologies that can satisfy dynamic service demands in the edge clouds. However, how to efficiently embed SFCs in the dynamic edge-cloud scenarios remains as a challenging problem. Considering different network topologies, we devise two deep reinforcement learning (DRL)-based methods for two network sizes: a deep deterministic policy gradient (DDPG) based method for the small-scale networks and an asynchronous advantage actor-critic (A3C) based approach for the large-scale networks. Simulation results demonstrate that our proposals can efficiently deal with the SFC-DMP in edge clouds and outperform the state-of-the-art methods in terms of the delay.