Network Function Virtualization (NFV) is a foundational technology for Mobile Edge Computing (MEC). It delivers network services by chaining Virtual Network Functions (VNFs) into sequential Service Function Chains (SFCs). One of the most critical challenges in MEC is how to provide continuous and stable services to high-mobility user, such as intelligent vehicles and drones. However, current mobility-aware SFC migration methods remain constrained by either post-hoc reaction or myopic prediction horizons, failing to reconcile the divergent timescales of network services and user mobility, thus resulting in suboptimal resource allocation and service delivery. In this paper, we first formulate the predictive mobility-aware SFC migration problem as an NP-hard Integer Linear Programming (ILP) problem. Aiming to mitigate service disruption for mobile users in MEC networks, we propose PreSFC, a predictive SFC migration framework that integrates multi-slot mobility forecasting with fine-grained network state tracking. We first design Gformer, a deep learning-based long sequence time-series forecasting model, which operates on short time slots (less than 200 ms) to sensitively capture network dynamics while predicting over multiple slots (e.g., 50 slots) to effectively track user mobility. This dual-scale design explicitly addresses the temporal disparity between mobility patterns and service requirements. Based on the predictions, we further propose an Optimal Sub-period Partitioning Migration (OSPM) algorithm to determine migration timing and locations. Extensive simulations show that our approach reduces the maximum and average downtime by approximately 55% and 40%, respectively, compared to benchmark methods.

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are expected to provide greater flexibility and manageability for next-generation IoT networks. In this context, network services should be modeled as Virtual Network Function Forwarding Graphs (VNF-FGs). A key challenge is efficient allocation of resources for sequentially arriving network service requests, a process known as VNF-FG placement. Most existing algorithms either manually or partially extract features from the physical network and VNF-FG or adopt a greedy approach, allocating resources as long as a feasible solution exists, which may over-allocate resources to VNF-FG requests, ultimately harming infrastructure providers’ long-term revenue. In this paper, we propose a VNF-FG placement and admission control algorithm based on hierarchical reinforcement learning, called EAC. It consists two levels of agents: a coarse-level agent that generates placement strategies and rejects requests with no feasible placement strategies, and a refine-level agent that implements admission control and rejects requests that are detrimental to long-term revenue. To fully capture the topological features of both the physical network and the VNF-FG, we employ a customized Graph Attention Network (GAT) that incorporates link feature awareness and enables deeper exploration. To fully explore historical temporal information for admission control, we construct state triples and feed them into a Recurrent Neural Network (RNN). Using Proximal Policy Optimization (PPO) as the foundational training algorithm, the corresponding agents are trained hierarchically. Extensive experimental results demonstrate that the proposed EAC algorithm outperforms existing state-of-the-art solutions in terms of acceptance rate, revenue-to-cost ratio, and long-term average revenue.

Ensuring the quality of service (QoS) in the Space-Air-Ground Integrated Network (SAGIN) requires implementing appropriate service function chain (SFC) embedding strategies to provide reliable general services and AI services. However, the complicated topology, limited infrastructure resources, and the heterogeneity in hardware and software in SAGIN hinder existing reliable embedding solutions from delivering high QoS. To address the challenges, this paper takes into account the heterogeneous reliable-aware SFC embedding (HRSE) problem in SAGIN. Specifically, we formulate the HRSE problem as a nonlinear integer programming problem that is NP-hard. Subsequently, an algorithm called HRSFCEA, which consists of deep reinforcement learning (DRL) and greedy rules, is proposed to tackle the challenges. Initially, leveraging the autonomous decision-making capability of DRL, SFC embedding is executed to select backups efficiently. The proposed algorithm reduces frequent trial and error by modifying its actions. Next, the paper gives the algorithm analysis including the complexity and unreliable environment. Finally, simulation results based on real-world datasets demonstrate that the DRL scheme converges to high rewards with minimal differences across varying numbers of hidden layer neurons. The proposed algorithm can improve the acceptance rate by up to 82% while incurring no more than 5% additional cost compared to the state-of-art algorithms.

As key enabling technologies for 5G, edge computing pushes computing resources to the edge close to IoT users, enabling low-latency services. Meanwhile, network function virtualization abstracts complex services into service function chains (SFCs) consisting of multiple virtualized network functions (VNFs), significantly simplifying service management. However, the highly dynamic traffic in edge environments may cause the routing configurations of SFC requests and the resource configurations of VNFs to become outdated. The outdated configurations may result in VNF load imbalance or overload, severely disrupting service availability and degrading user quality of service (QoS). Therefore, this paper studies the novel joint optimization of SFC routing update and elastic resource configuration problem, aiming to minimize the resource leasing cost of the service provider, while subject to multi-resource capacity and update delay constraints. Considering all these characteristics, we formalize this problem as an integer linear programming and prove its NP-hardness. To tackle this problem, we propose a rounding-based routing update and resource configuration algorithm to find cost-effective routing paths and configuration types for requests and VNFs. Further, we design a backtracking-based greedy improvement algorithm that upgrades VNF resource configurations and adjusts request routing paths to accommodate more requests, thus boosting network throughput. Extensive simulation results show that compared with state-of-the-art solutions, our scheme can reduce the leasing cost by 17.8%, while improving network throughput and meeting user QoS.

As a key enabler for 6G and symbiotic IoT applications, edge computing brings computing resources closer to end users, ensuring low-latency services, while network function virtualization (NFV) enables flexible service deployment by abstracting complex functionalities into service function chains (SFCs) composed of virtual network functions (VNFs). However, in dynamic edge environments, traffic changes and frequent network failures can lead to outdated routing configurations, resulting in load imbalance, network congestion, and Quality of Service (QoS) degradation. These issues will damage the robustness and real-time response capability required for symbiotic IoT systems. Although existing work separately optimizes VNF assignment or SFC routing for real-time and robust SFC updates, it fails to jointly optimize both, leading to potential VNF overload and throughput degradation. Therefore, this article studies the joint optimization of the VNF assignment and SFC routing problem, considering limited resource capacity, robustness and delay requirements, and bounded migration costs, aiming to maximize the network throughput. To capture all these characteristics, we formalize the problem as integer linear programming (ILP) and prove its NP-hardness. To tackle this problem, we propose a QoS-aware VNF assignment and routing update algorithm that first relaxes the ILP to linear programming (LP), and then randomly rounds the fractional solution obtained by solving LP to obtain a nearly optimal integer solution. Furthermore, we propose a backtracking-based greedy improvement algorithm, which greedily adjusts VNF assignments and request paths to accommodate more requests and satisfy all constraints. Extensive simulation results show that compared to state-of-the-art solutions, the proposed method can improve throughput by 22.98% while meeting user QoS.

For reliability, security and scalability, Multicast Request flows (MRs) need to traverse a Service Function Chain (SFC) that consists of series of Virtual Network Functions (VNFs) such as firewalls, encoder-decoder in Network Function Virtualization-enabled Software-Defined Networks (NFV-enabled SDNs). There are typically multiple identical VNF-instances in the network, it brings significant challenges when dynamically choosing or placing the requisite VNF-instances to construct a Service Function Tree (SFT) consisting of SFCs for fulfilling the MRs's routing. This paper investigates the Delay and Jitter Aware Dynamic SFT Embedding and Routing Problem (DJADSERP) considering VNF placement, network resources, delay and jitter constraints as well as network load balance in NFVEnabled SDNs. First, we formulate DJA-DSERP as an integer linear programming model and prove it to be NP-hard. Then, an auxiliary edge-weighted graph and an Optimal Link Selection Function (OLSF) are devised, and SFT Embedding Algorithm (SFT-EA) is proposed to address the problem aiming at minimizing the resource consumption costs while satisfying multiple QoS constraints and network load balance. Furthermore, we theoretically prove the effectiveness of the OLSF and the SFTEA. Simulation results demonstrate that the SFT-EA exhibits superior performance compared to existing algorithms in terms of throughput, traffic acceptance rate, and network load balance.

Advances in containerization technology and edge device performance enable applications to run on a wide range of devices through virtualization, enhancing service quality in decentralized edge networks. However, edge devices often lack the computational power of cloud infrastructure and may experience connection fluctuations, which makes node reliability crucial when providing Virtual Network Functions (VNFs). To provide Service Function Chain (SFC) which combines a series of ordered VNFs, it is necessary to determine the redundancy of VNFs to meet reliability requirement and decide whether to deal with these VNF requests immediately or defer them. Therefore, this paper addresses this problem and formulates it as a reliability-aware SFC scheduling problem in container environment (RASCE) and prove it to be NP-hard. To solve this problem, we propose a reliability-aware scheduling algorithm via priority-based node selection (SSAP) using Deep Reinforcement Learning (DRL), which consists of long-task prioritization redundancy strategy considering dynamic node reliability, priority-based node selection, and SFC scheduling based on DRL. The simulation demonstrates that our approach can enhance the success rate by a minimum of 5.78% in comparison to the state-of-the-art algorithm.

The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy. In this paper, we propose a novel hierarchically Federated Region-learning framework of Autonomous Vehicles (FedRAV), a two-stage framework, which adaptively divides a large area containing vehicles into sub-regions based on the defined region-wise distance, and achieves personalized vehicular models and regional models. This approach ensures that the personalized vehicular model adopts the beneficial models while discarding the unprofitable ones. We validate our FedRAV framework against existing federated learning algorithms on three real-world autonomous driving datasets in various heterogeneous settings. The experiment results demonstrate that our framework outperforms those known algorithms, and improves the accuracy by at least 3.69%. The source code of FedRAV is available at: https://github.com/yjzhai-cs/FedRAV.

The Internet of Vehicles (IoV) enhances data availability by equipping a plethora of sensors, driving the automotive industry towards data-driven Predictive Maintenance (PreM) models. However, traditional centralized PreM solutions, requiring complete access to training data, raise concerns about data privacy. PreM in the automotive domain is more challenging than in many other fields, partly due to the varying distribution nature of data samples and the limited network connectivity time caused by vehicle mobility. To address these challenges, we propose the PreM-FedIoV framework, extending single-agent Double Deep Q-Network (DDQN) to Multi-Agent Double Deep Q-Network (MADDQN). In each round, each vehicle client uploads a data packet to the server based on the current contention window, containing its local model, local test Mean Absolute Error (MAE), and a timestamp. The server initially performs federated aggregation on the received local models. The MADDQN module then dynamically adjusts the contention window of each vehicle for the next round based on the local test MAE and communication statistical state, aiming to optimize communication costs and predictive performance. Additionally, we utilize NS-3 to create IoV simulations and deploy the PreM-FedIoV framework within NS3-gym. We choose Federated Averaging (FedAvg) and FedAdam following the IEEE 802.11p standard as baselines. The experiments demonstrate significant improvements in our framework compared to state-of-the-art algorithms. On the C-MAPSS dataset, we achieve reductions of up to 10.2% in MAE, 26.31% in average communication clock time per round, and 65.6% in the number of participating clients per round. For the Random Battery Usage dataset, with up to 4.55%, 24.44%, and 36.58% improvements in the respective metrics.

Network function virtualization (NFV) is able to reduce the delay and improve the flexibility of network services in mobile edge computing (MEC) networks via deploying the service function chain (SFC) that consists of a sequence of ordered virtual network functions. However, it is still challenging to deploy SFC with delay guarantees and resource efficiency while taking into account the real-time network variations and dispersed edge server nodes in NFV/MEC-enabled networks. To address the issue, this paper proposes a self-attention mechanism-based double deep Q-network algorithm (SA-DDQN) for SFC deployment to jointly minimize the resource consumption on servers and bandwidth consumption on links within delay limits in dynamic NFV/MEC-enabled networks. In particular, we introduce the self-attention mechanism in the deep neural network structure, which enables the agent to pay its attention on more valuable physical nodes when making decisions, thus improving the efficiency of SFC deployment. Additionally, we utilize the Markov decision process (MDP) model to solve the problem of real-time network state variations. Finally, extensive simulation results show that our proposed SA-DDQN SFC deployment algorithm can reduce resource consumption by 25% and delay by 18.4% compared with the state-of-the-art algorithm.

Edge computing has been widely used in recent years for bringing services closer to end users, resulting in faster response for applications. However, the sensitive information that leaves the data owner is at risk of being disclosed because the service provider is generally honest-but-curious. Federated learning (FL) is a popular method for preserving privacy by transferring the model from the edge node to local devices and training on the local data set. Nonetheless, the training parameter that communicates between local mobile devices and the edge node may contain the original data and be guessed by adversaries. In order to address the privacy threats, we propose the PL-FedIPEC scheme in this article, which is a privacy-preserving and low-latency FL method that transmits parameters encrypted with the improved Paillier, a homomorphic encryption algorithm, to protect the privacy of end devices without transmitting data to the edge node. Our method introduces the improved Paillier encryption, which brings a new hyperparameter and previously computes multiple random intermediate values in the key generation phase so that the time for the encryption phase has a significant reduction. With this new algorithm, the time for model training is decreased, and the sensitive information is in ciphertext format and cannot be analyzed. To evaluate the efficiency of our proposed scheme, we conduct extensive experiments and the results validate and demonstrate that our scheme with the improved Paillier algorithm can achieve the same accuracy as the original Paillier algorithm and the baseline FedAVG algorithm. At the same time, our method can save a massive amount of time when training the learning model with various settings.

Software defined networking (SDN) with separated control plane and data plane brings new opportunities for traffic measurement in data center networks. However, in the SDN-enabled switches, available TCAM (Ternary Content Addressable Memory) resources for traffic measurement are limited. Thus, it is necessary to utilize traffic matrix (TM) estimation to derive a hybrid network monitoring scheme through combining the partial direct measurement offered by SDN with some inference techniques. Although large size flows play an important role in improving TM estimation accuracy, directly monitoring each flow and finding out large size flows consume massive channel bandwidth resource between control plane and data plane. Therefore, in this paper, we identify large size flows from multiple historical TMs instead of monitoring each flow. First, we analyze multiple historical TMs and observe that origin-to-destination (OD) pair whose flow size is selected as large size flow at last time slot is most likely to be selected for per-flow monitoring at next time slot, so these OD pairs are identified by gradient boosting machine and are directly regarded as sampled OD pairs in order to reduce resource consumption. Then, we propose a greedy heuristic algorithm to solve SDN-enabled switch selection problem to best utilize the TCAM resources and guarantee that most of sampled OD pairs are measured in the flow table. We also present a source node prefix tree based bit merging aggregation (SPTBMA) scheme to design feasible forwarding rules to be inserted in TCAM of SDN-enabled switches and reserve more TCAM space for sampled OD pairs. Finally, the experimental results based on real traffic dataset demonstrate that our proposed scheme outperforms the existing algorithms in terms of improving TM estimation accuracy and overcoming limitation of TCAM resources.

With the prosperity of cloud computing and video services, the demand for network resources has increased dramatically, leading to the remarkable growth in the amount of network energy consumption, a key factor restricting the development of data centers. Numerous existing works reduce network energy consumption by optimizing data transmission, but they ignore the energy consumption for data transmission preparation, such as activating devices and installing rules. In this paper, we jointly optimize device activation, rule installation and data transmission to minimize network energy consumption. Specifically, we first formulate the minimization problem of the energy consumption of device activation, rule installation, and data transmission. We then prove that it is NP-complete to get the optimal solution of the minimization problem, furthermore, we propose a heuristic algorithm to plan the path with minimum network energy consumption for each flow. The simulation results show that the energy consumption of our algorithm is close to the optimal solution solved by Gurobi, and our algorithm has lower complexity. Compared with the state-of-the-art algorithm, our algorithm always consumes less energy and has shorter flow completion time.

In order to solve the problem of flow scheduling in cluster computing framework, the scheduling strategy based on coflow has become a research hot spot. A coflow is a collection of data flows between two different stages of the same parallel computing task. Coflow scheduling in the case of unknown prior information depends on the data flow information of the sent part to infer the data size of coflow and allocate the scheduling sequence for coflow, which is easy to cause congestion. In this paper, we design an effective coflow scheduling mechanism namely, Classification According to Ports Number (CAPN). In the mechanism, firstly, coflows are quickly classified according to the Few Ports Number Scheduling First (FPSF) algorithm, and then coflows with different priorities are scheduled and adjusted, which greatly reduce the average coflow completion time (CCT). Simulation results show that compared with the classical Aalo and MCS mechanisms, our CAPN mechanism can reduce the completion time of coflow by by 31.32% and 25.72%, respectively.

Software middlebox-based services can be flexibly managed by software defined networking (SDN) and network function virtualization (NFV). Meanwhile, traffic routing can be simplified and the number of routing rules in the SDN-enabled switches can be reduced through the consolidated middlebox model. However, different network functions in middleboxes may alter the volume of processed traffic, so high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in the data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data planes. In this article, we first formulate this problem as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and prove its NP-hardness. Then, we design a two-phase algorithm to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, compared with the existing algorithms through extensive simulations, it demonstrates that our method has near-optimal controller load balancing and link load balancing performance and can improve response time by 9.7% compared with static scheme.

Due to that Service Function Chain (SFC) permits the forwarding of flows along a predetermined sequence chain of Virtual Network Functions (VNFs), it has become a common service in Network Function Virtualization (NFV)-enabled Software Defined Networks (SDNs). Generally, since there are multiple same VNF-instances in NFV-Enabled SDNs, this brings a great challenge for selecting or placing the required VNF-instances to satisfy the routing of SFC Request flows (SRs). In this paper, we study the routing problem for SRs by jointly considering dynamical VNF placement and multiple Resources and Quality of Service (QoS) constraints in NFV-Enabled SDNs. Specifically, we first define two optimization problems: one is the Dynamical VNF Placement and Routing Problem for SRs (DVPRP) and the other is the Delay, packet Loss and Jitter Aware Dynamical VNF Placement and Routing Problem for SRs (DLJA-DVPRP). We then formulate the two problems as Integer Linear Programming (ILP) problems. Next, we creatively devise an auxiliary edge-weight graph and propose two efficient algorithms to solve the problems with the aim of minimizing the resource consumption costs as well as ensuring multiple QoS constraints. Especially, we utilize the shortest path algorithm based on Lagrange relaxation method to solve the DLJA-DVPRP with multiple QoS constraints. Compared with existing algorithms, simulation results demonstrate our proposed algorithms have better performance in terms of throughput, traffic acceptance rate and load balance.

Software defined networking (SDN) and network function virtualization (NFV) can flexibly manage software middlebox based services, and the consolidated middlebox model is able to simplify traffic routing and reduce the number of routing rules in the SDN-enabled switches. However, different network functions in middleboxes may change the volume of processed traffics, thus high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data plane. This paper first formulates it as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and proves its NP-hardness. Then, a two-phase RL_RFRD algorithm is designed to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, the extensive simulation results demonstrate that the proposed algorithm has near-optimal controller load balancing and link load balancing performance and reduces response time by about 2x-5x compared with other algorithms.

Summary Data transmission in current networks is usually associated with strict priority enforcement for the purpose of quality of service (QoS). Under the case that priority flow requests are injected into the network sequentially without the information of future flow request arrivals, it is a challenging to achieve network throughput maximization for on‐line flow requests under the joint constraints of the flow's priority, bandwidth demand, and resource capacity. Software Defined Networking (SDN) can effectively solve the flow scheduling equilibrium problem between the priority of dynamic flow requests and the maximization of network throughput. Therefore, in this paper, we study on‐line flow request admission in SDN, the goal of which is to maximize the network throughput under the constraints of critical network bandwidth resources, flow priority, and bandwidth demands. First, we present the concept of flow routing cost and profit and a model to characterize the cost of using link resources and routing paths. Then, we propose an efficient on‐line priority flow scheduling algorithm (OPFSA) to solve priority flow request scheduling problem and analyze the competitive ratio of OPFSA. Our on‐line algorithm can reach throughput within of the highest possible throughput that can be achieved by an off‐line algorithm, where n is the number of node in the network. Finally, experimental results demonstrate that compared with SHORTEST‐SC, our proposed algorithm can enhance the cumulative bandwidth about 9% and 40% when general network size is 30 and 170 nodes, respectively, and improve the throughput about 25% in Fat‐tree network when pod size is 4.

We develop a hierarchical generative adversarial network (HGAN) for generating future typhoon cloud remote sensing images, which enables a visual means to typhoon cloud prediction. The HGAN consists of a global generator and a local discriminator. The global generator aims at producing the future typhoon cloud images as realistic as possible and accordingly reveals the structure and future location of the typhoon clouds. It is constructed in terms of a hierarchical architecture with multiple subnetworks, which capture the overall typhoon variations and favor generating clear future typhoon cloud images. The local discriminator tries its best to distinguish generated typhoon cloud images from ground-truth ones, based on the local patches. The local procedure encourages the discriminator to focus on characterizing the moving typhoon clouds rather than the still background. The global generator and the local discriminator are trained in an adversarial fashion with respect to historical typhoon cloud image sequences. The trained HGAN is capable of producing reliable visual predictions that are not only enabled by the global generator and but also examined by the local discriminator. Experiments validate the effectiveness of the HGAN for typhoon cloud prediction.

Multicast can improve network performance by eliminating sending unnecessary duplicated flows in the data center networks (DCNs), thus it can significantly save network bandwidth and improve the network Quality of Service (QoS). However, the network multicast blocking causes the retransmission of a large number of data packets, and seriously influences the traffic efficiency of data center networks, especially for the multicast traffic in the fat-tree DCNs owing to multi-rooted tree structure. In this paper, we propose a novel multicast scheduling strategy to reduce the network multicast blocking. In order to decrease the operation time of the proposed algorithm, therefore, the remaining bandwidth the selected uplink connecting to available core switch should be close to and greater the three times than the bandwidth of multicast requests. Then the blocking probability of downlink at next time-slot is calculated by using markov chain theory. Furthermore, we select the downlink with minimum blocking probability as the optimal path at next time slot. In addition, theoretical analysis shows that the blocking probability of scheduling algorithm is close to zero and has lower time complexity. Simulation results verify the effectiveness of our proposed multicast scheduling algorithm.