The scalability of joint routing and scheduling for time-triggered (TT) traffic in large-scale Time-Sensitive Networks (TSN) has become a critical bottleneck constraining the deployment scale of network applications. To address this challenge, this paper proposes the Dynamic Hypergraph Sparsification and Search (DHSS) framework, which innovatively transforms this scheduling problem into a Maximum Independent Set (MIS) problem on a dynamically constructed Hyper Conflict Graph (HCG). Addressing the NP-hard nature of this problem, we devise a novel semi-static sparsification architecture. This architecture decouples problem precision and solving efficiency by maintaining a ground-truth hypergraph (Hgt) for accurate constraint verification and a sparse working hypergraph (Hwork) for efficient computation. We employ a strategy combining periodic global Semidefinite Programming (SDP) reconstruction with low-overhead local updates to dynamically maintain the quality of the working hypergraph, thereby achieving an effective trade-off between solution quality and computational complexity. Comprehensive experimental evaluations under various network topologies and traffic loads demonstrate that the DHSS framework significantly outperforms current state-of-the-art methods in terms of both schedulability and scalability, offering an efficient and viable solution for the automated planning of large-scale industrial networks.

In order to meet the requirements of industrial Internet applications for network performance and reliability, the traffic scheduling in Time-Sensitive Networks (TSN) is particularly important. Although joint routing and scheduling has better schedulability than scheduling based on fixed routing, it requires enormous runtime. To improve the efficiency of solving the scheduling problem, this paper proposes a joint routing and scheduling algorithm based on deep reinforcement learning and A* (JRS-DRLA*). The JRS-DRLA* algorithm utilizes a deep reinforcement learning model known as Dueling DQN to model the influencing factors of scheduling effectiveness such as network load during the scheduling process. When selecting the action, the action masking will be performed to improve the learning efficiency of the model. In the aspect of algorithm design, the algorithm designs the reward function in the form of the heuristic function of the A* algorithm to reduce the end-to-end delay, and performs backtracking similar to A* when there is no valid action in the environment to help the model learn the optimal scheduling strategy. The experimental results show that this algorithm significantly improves convergence speed and shows enhancements in both runtime and end-to-end delay compared to similar algorithm.

Time-Sensitive Networking (TSN) provides deterministic services for diverse traffic types within a unified network. Among them, time-triggered (TT) traffic requires stringent timing guarantees, typically achieved through precise scheduling using Gate Control Lists (GCLs) in Time-Aware Shapers (TASs). However, most existing studies primarily focus on TT scheduling, often overlooking its impact on Audio Video Bridging (AVB) traffic, which demands worst-case delay (WCD) guarantees. This paper proposes an improved AVB-aware scheduling approach for TT traffic that enhances AVB performance without compromising TT schedulability. A rigorous network calculus analysis identifies two critical factors influencing WCD of AVB traffic: the maximum TT window length and the minimum relative offset between adjacent TT windows. Building on these insights, we develop a lightweight objective function for TT flow scheduling, enabling efficient evaluation of the impact on AVB traffic. This objective function is embedded into a Greedy Randomized Adaptive Search Procedure (GRASP)-based scheduling framework, further enhanced by a flow sorting strategy and a flexible local search mechanism that prioritizes high-impact TT flows and adaptively escapes local optima. Simulation results demonstrate that the proposed method significantly improves AVB performance and reduces runtime overhead, while consistently maintaining full TT schedulability across diverse TSN scenarios.

Last Level Cache (LLC) is a precious and critical resource that impacts the performance of applications running on top of CPUs. In this paper, we reveal the significant impact of LLC on the performance of the 5G user plane function (UPF) when running a cloudified 5G core on general-purposed servers. With extensive measurements showing that the throughput can degrade by over 50% when the precious LLC resource of UPF is not properly allocated, we identify three categories of performance degradation caused by incorrect LLC usage: DMA leakage problem, hot/cold mbuf problem and cache contention. To address these problems, we introduce the design and implementation of 5GC^2ache that monitors the LLC status as well as the throughput performance and dynamically adjusts key parameters of the LLC resource allocation. Our experiments show that 5GC^2ache enables a commercial 5G core to increase its throughput to 76.41Gbps, 39.41% higher than the original performance and 29.55% higher than the state-of-the-art.

With the growth of real-time application scenarios, the deployment of Time-Sensitive Networking (TSN) in dynamic scenarios has attracted more and more attention. However, since information such as the period, size, and arrival time of a new flow is uncertain, the previous scheduling results will seriously reduce the performance of subsequent scheduling. Most online scheduling methods will easily reach the bottleneck of flow scheduling, resulting in a decrease in schedulability. This paper analyzes the combinability of Time-Triggered (TT) flows and proposes the Possibility of Collision (PoC) as a measure of the degree of collisions on a link. In addition, we design a PoC-aware incremental scheduling and backtracking (PA/ISAB) mechanism to realize per-flow online deployment. The backtracking mechanism migrates the configured flows on the link based on the PoC, thereby alleviating the scheduling bottleneck while realizing the deployment of new flows. Meanwhile, we establish a PoC-aware pre-routing (PAPR) algorithm to improve schedulability. The evaluations verify that PA/ISAB can effectively reduce the execution time and reconfiguration overhead while ensuring high schedulability, thus realizing fast online deployment of the network.

Virtual Radio Access Network (vRAN) is an emerging approach offered by cloud providers to accelerate 5G services deployment. Despite significant microsecond-scale traffic variations, vRAN instances are provisioned based on peak load to meet strict latency requirements, leading to significant resource waste. Conceivably, vRAN can share CPUs with other applications to increase CPU utilization. Yet, existing sharing solutions require modifications to vRAN source code, hindering their deployment on public clouds. We present Aquifer, a microsecond-scale scheduler providing transparent CPU sharing for vRAN workloads. Our key observation is a common producer-consumer task execution pattern in mainstream vRAN implementations. We exploit this pattern to reclaim CPU cores from worker threads only at the boundary of processing different tasks. This guarantees run-to-completion task processing, which is critical for vRAN to achieve low latency and stability. Aquifer intercepts system calls invoked by vRAN at the OS layer to achieve transparent load monitoring and core reallocation. Aquifer employs a set of system-level optimizations on thread state detection, signal transmission and core selection, which reduces the scheduling cycle to 2 mu s. Experimental results show that Aquifer reclaims up to 88.31% of wasted CPU resources for two mainstream vRAN implementations, FlexRAN and OAI, without any source code modifications.

Time-Sensitive Networking (TSN) is a promising network technology that can ensure bounded latency and jitter for industrial real-time scenarios. It establishes the IEEE 802.1 Qbv standard to precisely control the periodic transmission time of the time-triggered (TT) traffic. However, it is still a challenge to efficiently schedule the TT traffic in large-scale networks with high transmission performance. In this paper, we propose a novel method called dynamic stream partitioning (DSP) to solve this problem. It satisfies the IEEE 802.1 Qbv standard and can significantly reduce the scheduling time by reducing the number of constraints. Based on the DSP method, we analyze the impact of stream partitioning on the transmission performance of the TT traffic, and propose an indicator called Normalized Degradation of Performance (NDOP) to quantify it dynamically. Furthermore, we design a partitioning-aware dynamic routing (PADR) to expand the scheduling space. Integrated with NDOP and PADR, we propose a joint routing and dynamic stream partitioning (JR/DSP) algorithm. Extensive simulation experiments verify that the proposed JR/DSP algorithm has higher scalability, transmission performance, and schedulability compared to the static stream partitioning algorithms.

Time-Sensitive Networking (TSN) is an emerging technology with broad application prospects that meets the real-time deterministic communication needs in the field of industrial networks. The time-triggered (TT) stream is a type of traffic in TSN whose reliability is critical. By introducing redundant transmission, the reliability of the stream can be improved, but it will consume more network resources and affect the success rate of scheduling. This paper focuses on the goal of flexible positioning in IEEE 802.1CB, demonstrating the necessity and effectiveness of replicating and eliminating frames in relay systems for reliability. We propose a reliability-aware segmented redundant routing and scheduling (RSRRS) mechanism that precisely satisfies the various reliability requirements of TT streams in industrial networks by prioritizing the redundancy protection of the links with the highest failure rate. The experimental results show that the RSRRS mechanism significantly reduces bandwidth consumption and improves solving efficiency while ensuring high reliability satisfaction rates compared with baseline methods.

Time-Sensitive Networking (TSN) is a new technology developed from Ethernet that guarantees deterministic transmission of various types of flows, such as Time-triggered (TT) flows and Audio-video-bridging (AVB) flows, in the same network. Currently, Time-aware Shaping (TAS) and Credit-based Shaping (CBS) are widely used for scheduling hybrid flows, where the credit value in CBS is directly linked to the logical bandwidth value idleSlope, , which affects the real-time performance of AVB flows. However, as the network scale increases, existing bandwidth allocation methods result in higher computation times and lower allocation success rates. In this article, the aforementioned problem is addressed by two-step: first, we design a load-balanced routing heuristic (LBRH) to improve the allocation success rate; second, we accelerate the CBS bandwidth allocation by using unified bandwidth allocation scheme, which integrates LBRH and further improves the allocation success rate. Performance evaluation in multiple test cases shows that LBRH can improve the success rate of bandwidth allocation, and the unified bandwidth allocation scheme integrating LBRH significantly reduces the overall execution time while improving the success rate of bandwidth allocation, which is more suitable for large-scale TSN networks with complex routing.

Spiking neural networks (SNNs) based on the leaky integrate and fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the bioplausible neuronal dynamics and simplicity, LIF-SNN benefits from event-driven processing, however, usually face the embarrassment of reduced performance. This may because, in LIF-SNN, the neurons transmit information via spikes. To address this issue, in this work, we propose a leaky integrate and analog fire (LIAF) neuron model so that analog values can be transmitted among neurons, and a deep network termed LIAF-Net is built on it for efficient spatiotemporal processing. In the temporal domain, LIAF follows the traditional LIF dynamics to maintain its temporal processing capability. In the spatial domain, LIAF is able to integrate spatial information through convolutional integration or fully connected integration. As a spatiotemporal layer, LIAF can also be used with traditional artificial neural network (ANN) layers jointly. In addition, the built network can be trained with backpropagation through time (BPTT) directly, which avoids the performance loss caused by ANN to SNN conversion. Experiment results indicate that LIAF-Net achieves comparable performance to the gated recurrent unit (GRU) and long short-term memory (LSTM) on bAbI question answering (QA) tasks and achieves state-of-the-art performance on spatiotemporal dynamic vision sensor (DVS) data sets, including MNIST-DVS, CIFAR10-DVS, and DVS128 Gesture, with much less number of synaptic weights and computational overhead compared with traditional networks built by LSTM, GRU, convolutional LSTM (ConvLSTM), or 3-D convolution (Conv3D). Compared with traditional LIF-SNN, LIAF-Net also shows dramatic accuracy gain on all these experiments. In conclusion, LIAF-Net provides a framework combining the advantages of both ANNs and SNNs for lightweight and efficient spatiotemporal information processing.

Network function virtualization (NFV) is a promising technology that decouples network functions from hardware. Connecting virtual network functions (VNFs) in series to form a service function chain (SFC) can flexibly orchestrate and expand network functions. However, there are higher availability requirements for SFCs. This paper aims to solve the SFC placement problem under availability and resource constraints. This paper proposes the sideway cross (SC) backup model, which considers the availability of both VNFs and physical machines (PMs) in a data center. The SC model cross-arranges the backup instances of VNFs to guarantee availability and optimize resource consumption. Then, this paper proposes the heuristic meteor shower optimization (MSO) algorithm to place SFCs. Compared to traditional heuristic algorithms, MSO can improve the execution time by approximately 200%. Combined with the SC backup model, MSO can effectively improve the availability and resource overhead. The evaluation results show that the proposed approach can guarantee higher availability and consumes fewer resources. The proposed approach only needs 75% of the resources to achieve the same availability as the state-of-the-art models.

Network Function Virtualization (NFV) is a promising paradigm that separates network functions from proprietary devices. Network service in NFV-enabled networks is achieved as a Service Function Chain (SFC), consisting of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within dynamic networks is a key challenge. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the concurrent migration of VNFIs based on the actual network situation. We first formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected services and to guarantee network load balancing after the migration simultaneously. To this end, we prove the NP-hardness of this problem and propose the Improved Hybrid Genetic Evolution (IHGE) algorithm to address it. Besides, to reduce the computation overhead of IHGE for large-scale networks, a multi-stage heuristic algorithm based on optimal order (MSH-OR) is designed. Finally, we perform a side-by-side comparison with prior algorithms. Extensive evaluation shows that the proposed approaches can effectively reduce the average delay for different scale networks while ensuring network load balancing.

Network Function Virtualization (NFV) brings flexible provisioning and great convenience for enterprises outsource their network functions to the Data Center Networks (DCNs). Network service in NFV is deployed as a Service Function Chain (SFC), which includes an ordered set of Virtual Network Functions (VNFs). However, in one SFC, the SFC delay increases linearly as the length of SFC increases. SFC parallelism can achieve high performance of SFC. In this article, we focus on the parallelized SFC placement problem in DCN considering availability guarantee and resource optimization. Firstly, we define the parallelized SFC and propose a multi-flow backup model. The parallelized SFC consists of multiple parallelized sub-SFCs. We split large data flow into multiple small sub-flows, each of them can be transmitted in one sub-SFC. The backup model provides backup sub-SFCs for working sub-SFCs to improve availability. Finally, we design three placement strategies and a Hybrid Placement Algorithm (HPA) aimed at mapping SFCs to DCN. Evaluation results show that our proposed solutions outperform the related work. We can reduce SFC delay (30%) and optimize link consumption (reduce 40%) while guaranteeing availability (99.999%).

In the traditional distributed control network, due to the difficulty in detection and the ambiguous defense responsibility, it is not efficient and effective to detect Distributed Denial of Service (DDoS) attacks in the network where they are launched, which is so-called source-based defense mechanism. Moreover, with the development of cloud computing, Internet of Things (IoT), and mobile Internet, the number of terminals and the communication bandwidth in a single autonomous domain have increased significantly, providing much more easy conditions for organizing large-scale botnets to launch a threatening DDoS attack. Therefore, there is an urgent need for source-based defense against DDoS attacks. The emerging Software-Defined Networking (SDN) provides some new ideas and advantages to solve this problem, such as centralized control and network programmability. In this paper, we proposed a defense method based on sFlow and improved Self-Organizing Map (SOM) model in SDN. This method consists of an sFlow-based macro-detection, which could cover the entire network to perceive DDoS attacks, a SOM-based micro-detection, which is used to recognize the attack traffic, and a response strategy based on the global view given by the controller. The experimental results under open data and simulated attack scenarios have proved the effectiveness of the proposed method, and it also has better overall detection performance than k-means and k-medoids.

Data integrity is a core security issue in reliable cloud storage that has received widespread attention. Data auditing protocols enable verifiers to efficiently check the integrity of outsourced data without downloading the data. A key research challenge associated with the design of existing data auditing protocols is the efficiency of the auditing process. Since existing protocols tend to auditing all cloud data, in fact, some data may have just been used or will be used soon, then auditing all these data is invalid, which is a waste of resources. In this paper, we attempt to address the waste of resources due to invalid auditing in cloud data integrity checking by introducing user behavior prediction algorithms,the first in such an approach, to the best of our knowledge. More specifically, we introduce the concept of valid auditing in data integrity verification based on the concept of valid auditing. We formalize a system model and a security model for this new concept. Then, using user behavior prediction algorithms, we give methods to improve auditing efficiency and reduce invalid auditing. Finally, we develop a prototype implementation of the protocol to validate the practicality of the scheme.

As current develop trends in network architecture and key technologies are enabling the vision of the heterogeneous sharing network, the necessity intensifies for addressing real-life aspects in massive scales. Effectively control the network topology remains a key design requirement for heterogeneous resource sharing network. In this work, we exploit previous theoretical results on the network topology control and the decentralized nature of the blockchain, proposing a network topology control algorithm based on a positive evaluation method for resource sharing network, and was demonstrated for controlling a dynamic network topology. Furthermore, we established the positive evaluation method to evaluate the node's reliability by virtual credit values and behavior of the nodes. The simulation results and performance analysis showed that the proposed algorithm can effectively delay the expansion speed of network topology, thereby increase the feasibility and effectiveness of topology control.

Nowadays, network softwareization is an emerging techno-economic transformation trend that significantly impacts how enterprises deploy their network services. As an essential technology in this trend, Network Function Virtualization (NFV) enables scalable and inexpensive network services by flexibly instantiating Virtualized Network Functions (VNFs) on commercial-off-the-shelf devices. In this paper, we focus on the VNF placement problem in NFV-enabled networks, aiming to maximize the number of accepted Service Function Chain Requests (SFCRs) while guaranteeing their delay requirements. To improve resource utilization efficiency, we take account of Fundamental Resource Overheads (FROs) and the shareability of VNF instances. We mathematically formulate the VNF placement problem and propose the Throughput Optimization and Delay Guarantee (TO-DG) heuristic solution, consisting of an affinity-based SFCR mapping algorithm and a VNF request adjustment algorithm. The evaluation results show that the performance of TO-DG is near to results derived by ILP solver for small scale problems. Moreover, TO-DG obtains higher network throughput than contrasting schemes in different scenarios and significantly improves network resource utilization.

Network Function Virtualization (NFV) brings flexible provisioning and great convenience for enterprises outsource their network functions to the Data Center Networks (DCNs). Network service in NFV is deployed as a service chain, also known as Service Function Chain (SFC), which includes an ordered set of Virtual Network Functions (VNFs). However, there might be some small size flows being queued behind the large flows in one SFC, resulting in network congestion and high SFC delay. In this paper, we focus on the parallelized SFC placement problem in DCN considering availability guarantee and resource optimization. Firstly, we define the parallelized SFC and propose a multi-flow backup model. Then, we design three placement strategies and a Hybrid Placement Algorithm (HPA) aiming at mapping SFCs to DCN. Compared with the existing approaches, our proposed solutions can reduce SFC delay and optimize link consumption while guaranteeing availability.

Sensitive data protection is essential for mobile users. Plausibly Deniable Encryption (PDE) systems provide an effective manner to protect sensitive data by hiding them on the device. However, existing PDE systems can lose data due to overriding the hidden volume, waste physical storage owing to the "reserved area" for avoiding data loss, and require device reboot when using the hidden volume. This paper presents MobiGyges, a hidden volume-based mobile PDE system, to fill the gap. MobiGyges addresses the problem of data loss by restricting each storage block used only by one volume, and it improves storage utilization by eliminating the "reserved area". MobiGyges also avoids device reboot by mounting the hidden volume dynamically on-demand with the Dynamic Mounting service. Moreover, we identify two novel PDE oriented attacks, the capacity comparison attack and the fill-to-full attack. MobiGyges can defend against them by jointly leveraging the Shrunk U-disk method and multi-level deniability. We implement the MobiGyges proof-of-concept system on a real mobile phone Google Nexus 6P with LineageOS 13. Experimental results show that MobiGyges prevents data loss, avoids device reboot, improves storage utilization by over 30% with acceptable performance overhead compared with current solutions. (C) 2020 Elsevier B.V. All rights reserved.