Bandwidth Resources Research Articles

Joint task assignment and resource allocation in VFC based on mobility prediction information

Vehicular Fog Computing (VFC) is a novel architecture that utilizes end-user clients or near-user edge devices to reduce the processing and transmission time of vehicular tasks. However, the high mobility of vehicles in VFC presents a challenge in coordinating vehicles and managing multi-dimensional resources. In this paper, we investigate the joint task assignment and resource allocation optimization problem by incorporating mobility prediction information. We propose two schemes that consider execution speed and accuracy. The first scheme decomposes the original problem into two sub-problems: subtask assignment and bandwidth resource allocation, solved by matching and convex optimization methods. The second scheme uses an iterative optimization process based on 0–1 integer linear programming. Simulation results based on real-world mobility datasets demonstrate the effectiveness and complementarity of the proposed schemes.

ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows

Information-centric networking (ICN) is a promising solution that can meet the challenges of IP-based networks. ICN adopts the solution of separating name and address to solve the dual semantics problem of IP addresses, which has natural advantages in supporting mobility. These mobility support methods in ICN have realized route optimization to a certain extent. In fact, due to these methods not considering the real-time network environment, such as the bandwidth of the switching path and the characteristics of different mobile data flows, these approaches adopt the same routing optimization solution for all data flows generated by mobile users, resulting in a single switching path. The bandwidth of a single switching path may not meet the transmission requirements of mobile data flows, which will greatly affect the performance of mobile users. Therefore, how to efficiently allocate switching paths for multiple mobile data flows generated by mobile users is an urgent problem in ICN mobility support. In this paper, we first propose a mobility support method for dynamically allocating mobile data flows (DAFM) based on the ICN mobility support architecture and model the mobile data flow allocation problem as a late-binding node selection problem. We then propose a double-constraints heuristic algorithm (DCHA) based on the Moser algorithm. By simultaneously adjusting the bandwidth resource constraints of the switching path and the space resource constraints of the Mobility Flow Table (MFT), we achieve late-binding node selection to minimize average packet delay. Finally, experimental results show that our proposal can achieve mobility support. Under the guarantee of a low-latency Name Resolution System (NRS), our method outperforms other methods in terms of average packet delay, packet loss rate, and handover delay.

A Cloud Computing Based Deep Compression Framework for UHD Video Delivery

Ultra-high-definition (UHD) videos has increased popularity. However, the data size of UHD videos is 4-16 times larger of HD videos. This brings many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. In this paper, we propose a cloud computing based deep compression framework named Pearl, which utilizes the power of deep learning and cloud computing to compress UHD videos. Pearl compresses UHD videos from two respects: the frame resolution and the colorful information. In pearl, an optimal compact representation of the original UHD video is learned with two deep convolutional neural networks (DCNNs): super resolution CNN (SR-CNN) and colorization CNN (CL-CNN). SR-CNN is used to reconstruct a high-resolution video from a low-resolution video, while CL-CNN is adopted to preserve the color information of the video. Pearl focuses on video content compression in two new directions. Thus, it can be integrated with any existing video compression system. We evaluate the performance of Pearl with a wide variety of network conditions, quality of experience metrics, and video properties. In all considered scenarios, Pearl can further compress 84% of video size and reduce 73% of network transmission latency.

Joint Beam Management and Power Allocation in THz-NOMA Networks

This paper investigates how to apply non-orthogonal multiple access (NOMA) as an add-on in terahertz (THz) networks. In particular, prior to the implementation of NOMA, it is assumed that there exists a legacy THz system, where spatial beams have already been configured to serve legacy primary users. The aim of this paper is to study how these pre-configured spatial beams can be used as a type of bandwidth resources, on which additional secondary users are served without degrading the performance of the legacy primary users. A joint beam management and power allocation problem is first formulated as a mixed combinatorial non-convex optimization problem, and then solved by two methods with different performance-complexity tradeoffs, one based on the branch and bound method and the other based on successive convex approximation. Both analytical and simulation results are presented to illustrate the new features of beam-based resource allocation in THz-NOMA networks and also demonstrate that those pre-configured spatial beams can be employed to improve the system throughput and connectivity in a spectrally efficient manner.

P‐4.3: An Adaptive Foveal Image Transmission Algorithm Based on Human Eye Gaze Point

With the development of display devices towards high resolution and high refresh rate, the amount of video image data is increasing exponentially, which brings challenges to the bandwidth resources of the display system. From the perspective of human visual system, combining the change of the gaze point position and the sampling characteristics of the human eye foveal, an adaptive foveal image transmission algorithm based on human eye gaze point is proposed to reduce the data transmission bandwidth. The experimental results show when the viewing distance is 2 and the gaze point position is at the center of the image. The comprehensive compression ratio is 12.257, the data transmission clock frequency is 12.532MHz, and the total amount of data transmitted is 8.158% of the original total. The total amount of data transmission is greatly reduced, and the pressure on data transmission bandwidth is effectively relieved. So that video images with higher resolution and refresh rate can be displayed under the same clock frequency.

WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360{\deg} video frames to VR users. Specifically, deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guarantee adequate transmission reliability and resilience under various channel states. Moreover, implementation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR.

Merging engine implementation for intra-frame sharing in multi-tenant virtual passive optical networks

High up-front capital expenditures impede the widespread deployment of passive optical networks (PONs). A multi-tenant solution, in which multiple network operators virtually share PON infrastructure and bandwidth resources, can result in significant cost savings. First, we investigate the viability of virtual PONs, in which virtual network operators share a portion of the upstream bandwidth in a frame. Each PON schedules its frame-level capacity using a dedicated dynamic bandwidth assignment algorithm, resulting in the generation of an independent bandwidth map (BMap). Second, we implement at the optical line terminal a merging engine that combines individual virtual BMaps into a single physical BMap, which is then transmitted to all optical network units. Our novel traffic merging algorithm is capable of consolidating traffic across all transmission containers. We apply this merging engine on top of the 10-Gbit-capable PON module and validate our approach using network simulator 3. The results show that our proposed intra-frame sharing technique, when integrated with our traffic merging algorithm, significantly outperforms the standard inter-frame sharing approach in terms of both latency and packet loss. Furthermore, we observed that best-effort traffic is affected most for all resource constraint scenarios of intra-frame sharing.

Energy Consumption and QoS-Aware Co-Offloading for Vehicular Edge Computing

By deploying computing, storage, and bandwidth resources at the user side, vehicular edge computing (VEC) provides low-delay services for vehicle users. However, due to the limited resources of edge servers, how to efficiently meet the Quality-of-Service (QoS) requirements of multiple tasks and save the total energy consumption in a dynamic environment is an important issue in VEC. In this article, we first propose an energy consumption and QoS-aware co-offloading model. Unlike most previous studies, our goal is to minimize the total energy consumption while guaranteeing the QoS constraints of tasks, thus avoiding the overallocation of resources and high energy consumption caused by the one-sided pursuit of delay minimization. Then, without the requirements for domain experts, we propose Bayesian optimization-based computation offloading (BOCO) method to find the optimal offloading decision. To the best of our knowledge, this work is the first to apply Bayesian optimization to computation offloading in VEC. Furthermore, we conduct a series of experiments and comparisons with other offloading methods to analyze the effectiveness and performance of the proposed algorithm. Experimental results verify that our proposed BOCO outperforms counterparts.

A Resilient Adaptive Event-triggered H\(\infty\) Tracking Control of T-S Fuzzy Systems with Markovian Jump Parameters under DOS Attacks

In this paper, the problem of H\(\infty\) output-tracking control for T-S fuzzy markov jump systems(MJSs) with imperfect premise matching is investigated. The conditional probability information between the system and the controller is imperfectly known. Firstly, due to the in influence of network-induce delay, the system mode information can not be transmitted to the controller synchronously, thus, a discrete-time hidden markov model(HMM) is established to describe the asynchronous phenomenon between the system and the controller; Secondly, form the prospectively of practical applications, packet loss in two channels is taken into consideration, which is caused by denial-of-service(DOS) attacks; Thirdly, in order to improve the data transmission efficiency and save network bandwidth resources, a novel resilient adaptive event-triggered(AET) mechanism is proposed, in which the event-based threshold parameter is dynamically regulated over the processing. Additionally, resorted the compensation principle and the lyapunov-krasovski(L-K) functions methods, some sufficient conditions in form of liner matrix inequality(LMI) are achieved to co-guarantee the stability and H\(\infty\) tracking property of the closed-loop system; Finally, two examples are given to verify the effectiveness of our design mentality.

ReTSN: Resilient and Efficient Time-Sensitive Network for Automotive In-Vehicle Communication

Time-sensitive networking (TSN) is being widely investigated to provide Ethernet capabilities for in-vehicle backbone communication. However, the gate control list (GCL), as a simple mechanism for achieving timing determinism for safety-critical traffic (ST) frames with hard deadlines, is too rigid to handle the intrinsic timing uncertainty of automated driving systems (ADSs). Due to the complexity and the unpredictable operating environment, there can be delayed ST frames that can disrupt the solidly fixed timing behavior. In this work, we present one novel approach to effectively use bandwidth resources to deal with delayed ST frames, which cannot be handled by a traditional fixed GCL, and the discarding of them should be reduced to improve the system’s integrity. An acceptance test is implemented to report to the application layer when an ST frame will miss its deadline and hence be rejected, i.e., prevented from entering the switch. To further improve the efficiency of bandwidth usage, we investigate how to improve the performance of the more important Class A frames in an audio-video-bridging (AVB) switch, which adopts a credit-based shaper mechanism, and we propose a constant bandwidth server to replace the credit-based shaper while taking fairness into consideration. Evaluation with extensive experiments shows that both resilience and efficiency of the TSN are significantly enhanced compared with a credit-based shaper, especially when the traffic load is relatively high. For the delayed ST frames and event-triggered traffic, our approach is able to schedule more than the solution using the AVB switch even with a high network load.

A Border Gateway Protocol LRA-BGP for Integrated Satellite-terrestrial Networks

With the rapid development of information technology, the demand for multi-dimensional integrated information resources of air, sky, and ground in the future information service industry is increasing day by day. The integrated satellite-terrestrial networks will become an important development trend in my country’s communication network in the future. However, the movement of satellites will cause frequent topology changes, which will seriously affect the overall performance of the terrestrial network. Aiming at this problem, this paper proposes a lightweight route advertisement border gateway protocol LRA-BGP. LRA-BGP protects terrestrial networks from the high dynamics of satellites by reducing the number of advertised routes. In addition, a real network environment is also built for actual testing. The results show that LRA-BGP can reduce the bandwidth resource occupation of the ground network by 26.9%~68.8% and CPU usage by 6.17%~7.31%.

GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication

Recently, federated learning (FL) techniques have enabled multiple users to train machine learning models collaboratively without data sharing. However, existing FL algorithms suffer from the communication bottleneck due to network bandwidth pressure and/or low bandwidth utilization of the participating clients in both centralized and decentralized architectures. To deal with the communication problem while preserving the convergence performance, we introduce a communication-efficient decentralized FL framework GossipFL. In GossipFL, we 1) design a novel sparsification algorithm to enable that each client only needs to communicate with one peer with a highly sparsified model, and 2) propose a new and novel gossip matrix generation algorithm that can better utilize the bandwidth resources while preserving the convergence property. We also theoretically prove that GossipFL has convergence guarantees. We conduct experiments with three convolutional neural networks on two datasets (IID and non-IID) under two distributed environments (14 clients and 100 clients) to verify the effectiveness of GossipFL. Experimental results show that GossipFL takes less communication traffic for 38.5% and less communication time for <inline-formula><tex-math notation="LaTeX">$49.8$</tex-math></inline-formula> % than state-of-the-art solutions while achieving comparative model accuracy.

An adaptive heuristic algorithm to solve the network slicing resource management problem

SummaryArtificial intelligence‐based (AI‐based) network slicing bandwidth allocation enables the 5G/6G service providers to create multiple virtual networks atop a shared physical infrastructure while fulfilling varying end‐user demands. Some researchers argue that AI‐enable network may run the danger of having private information compromised. We still need a backup rule‐based methodology to allocate bandwidth resource to each slice, if the AI‐based method suddenly encounters security issues. To design such a rule‐based methodology, this study attempts to answer two questions: (1) Is the network slicing bandwidth allocation problem the nondeterministic polynomial‐time completeness (NP‐completeness)? (2) Is there a heuristic methodology without any training process, which has equivalent performance compared to the AI‐based methodology? This study first proves the classical network slicing bandwidth allocation problems is NP‐completeness. This shows that the designed heuristic method is inescapably suboptimal to the network slicing bandwidth allocation problem. Secondly, this study proposes the Adaptive Hungarian Algorithm (AHA), which outperforms previous AI‐empowered method and does not need any training process. The experiments demonstrate that AHA reached 93%–97% of the maximal system throughput by brute‐and‐force algorithm, compared to other methodologies only having at most 93% of the maximal system throughput. This also indicates that AHA is capable to solve the network slicing bandwidth allocation problem, if the telecommunication operators do not have sufficient sample complexity to train an AI model.

A Location-Aware Resource Optimization for Maximizing Throughput of Emergency Outdoor–Indoor UAV Communication with FSO/RF

In emergency communication scenarios, unmanned aerial vehicles (UAVs) can be used as an air relay to provide higher-quality communication for indoor users. When bandwidth resources are scarce, the use of free space optics (FSO) technology will greatly improve the resource utilization of the communication system. Therefore, we introduce FSO technology into the backhaul link of outdoor communication, and use free space optical/radio frequency (FSO/RF) technology to realize the access link of outdoor indoor communication. The deployment location of UAVs will affect not only the through wall loss of outdoor-indoor communication but also the quality of FSO communication, and, therefore, it needs to be optimized. In addition, by optimizing the power and bandwidth allocation of UAVs, we realize the efficient utilization of resources and improve the system throughput on the premise of considering information causality constraints and user fairness. The simulation results show that, by optimizing the location and power bandwidth allocation of UAVs, the system throughput is maximized, and the throughput between each user is fair.

A bidirectional DNN partition mechanism for efficient pipeline parallel training in cloud

Recently, deep neural networks (DNNs) have shown great promise in many fields while their parameter sizes are rapidly expanding. To break through the computation and memory limitation of a single machine, pipeline model parallelism is proposed for large-scale DNN training by fully utilizing the computation and storage power of the distributed cluster. Cloud data centers can also provide sufficient computing, storage and bandwidth resources. However, most existing approaches apply layer-wise partitioning, which is difficult to obtain an even model partition result because of the large computational overhead discrepancy between DNN layers, resulting in degraded efficiency. To tackle this issue, we propose “Bi-Partition”, a novel partitioning method based on bidirectional partitioning for forward propagation (FP) and backward propagation (BP), which improves the efficiency of the pipeline model parallelism system. By deliberated designing distinct cut positions for FP and BP of DNN training, workers in the pipeline get nearly equal computational loads, and the balanced pipeline fully utilizes the computing resources. Experiments on various DNN models and datasets validate the efficiency of our mechanism, e.g., the training efficiency achieving up to 1.9times faster than the state-of-the-art method PipeDream.

Integrated Sensing and Communication Aided Dynamic Resource Allocation for Random Access in Satellite Terrestrial Relay Networks

An integrated sensing and communication (ISAC) aided dynamic resource allocation is proposed for random access (RA) in Internet-of-Things (IoT) oriented satellite terrestrial relay networks (STRNs). Specifically, we design an ISAC aided RA architecture, which consists of sensing phase and RA phase. Relays can sense the number of active users by wireless sensing and assist RA with the sensed results. Active users are grouped into orthogonal clusters in the spectrum domain according to relays that they belong to. To appropriately utilize the bandwidth resources, a dynamic resource allocation optimization problem is formulated and solved, which is able to dynamically allocate bandwidth to each cluster according to the number of intra-cluster active users. Based on allocated bandwidth, active users can always perform optimal uplink RA, which maximizes the total system throughput. Simulation results show that the proposed scheme greatly outperforms the existing schemes in terms of total system throughput and packet loss rate (PLR).

Trust in Edge-based Internet of Things Architectures: State of the Art and Research Challenges

The Internet of Things (IoT) aims to enable a scenario where smart objects, inserted into information networks, supply smart services for human beings. The introduction of edge computing in IoT can reduce the decision-making latency, save bandwidth resources, and expand the cloud services to be allocated at the network’s edge. However, edge-based IoT systems currently face challenges in their decentralized trust management. Trust management is essential to obtain reliable mining and data fusion, improved user privacy and data security, and provisioning of services with context-awareness. In this survey, we first examine the edge-based IoT architectures currently reported in the literature. Then a complete review of trust requirements in edge-based IoT systems is produced. Also, we discuss about blockchain as a solution to solve several trust problems in IoT and analyze in detail the correlation between blockchain and edge computing. Finally, we provide a detailed analysis of performance aspects of trusted edge-based IoT systems and recommend promising research directions.

Joint VNF Placement, CPU Allocation, and Flow Routing for Traffic Changes

The emerging network-softwarization technologies, such as software-defined networking and network function virtualization play important roles in 5G communication and future networks. One of the critical challenges of the practical application of the softwarized networks is to appropriately place virtual network functions (VNFs). The underlying resources and traffic requirements are often factored in the previous works of VNF placement. However, VNFs’ dynamic abilities of changing traffic are usually ignored. Resources allocated to VNFs can vary their traffic-change ratios, and the adjustment of resource volumes should be provisioned to support traffic changes. In this work, we pay attention to the joint optimization problem of VNF Placement, CPU Allocation, and flow Routing (VNFPAR) in the scenarios consisting of VNFs that can dynamically change traffic. We employ the logarithmic functions to approximate VNFs’ traffic change relations and formulate VNFPAR as a mixed-integer nonlinear programming (MINLP) problem. We demonstrate that this problem is highly nonconvex and involves highly coupled variables. For small-scale VNFPAR problems, we propose an optimal algorithm based on relaxation and programming to consume the minimum bandwidth resources. Because VNFPAR is NP-hard, to quickly find near-optimal solutions for large-scale VNFPAR problems, we present heuristic algorithms based on multistage greedy and simulated annealing, respectively. Besides, to achieve a tradeoff between solution quality and execution time, we decompose VNFPAR into subproblems and design an alternating optimization-based method. We evaluate our algorithms on real-work topologies and traffic patterns. Extensive simulations show that our proposed heuristic algorithms are convergent, stable, and effective in terms of solving VNFPARs. The proposed algorithms have small optimality gaps within 7.4%–26.3%. Meanwhile, they save 39.3%–48.4% bandwidth resources compared with relevant baseline technologies.

Optimizing communication and computational resource allocations in network slicing using twin-GAN-Based DRL for 5G hybrid C-RAN

The network slicing defined from 3GPP Rel. 15 is one important feature and function for 5G networks. In this paper, a new machine learning scheme is proposed by extending existing generative adversarial network (GAN) based deep reinforcement learning (DRL) result, namely Twin-GAN-based DRL (TGDRL) scheme, by utilizing two GAN-based DRLs to jointly allocate wireless bandwidth resources and computational resources. Existing resource allocation results are just only consider the bandwidth allocation, or just only consider the computational resource allocation. The main contribution of the proposed TGDRL scheme is to simultaneously investigate the bandwidth allocation and computational resource allocation by utilizing a multi-objective optimization algorithm, which aims to improve the efficiency of spectrum and reduce the consumption of computational resources. In our simulation, the total delay, the spectral efficiency, and the computational consumption of our proposed scheme is improved by 10.2%, 15.7%, and 12.8%, compared to existing schemes.

Multi-Keyword Searchable and Verifiable Attribute-Based Encryption Over Cloud Data

In cloud data sharing systems, Searchable Encryption (SE) schemes ensure data confidentiality with retrieving, but it faces several issues in practice. First, most of the previous Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) systems enable users to initiate search requests with a single keyword, which results in many inaccurate results to be returned, thereby wasting computing and bandwidth resources. Second, untrusted cloud servers may return a small portion of incomplete search results to compress communication overhead. Besides, most CP-ABKS schemes only support an unshared multi-owner setting, which incurs a large amount of computational and storage overhead. Furthermore, when the keyword space is a polynomial, most of the previous schemes suffer from offline keyword guessing attacks. To address these issues, we focus on a multi-keyword search scheme which supports the verification of search results without losing efficiency by combining Ciphertext Policy Attribute-Based Encryption (CP-ABE) technology under the shared multi-owner mechanism. We show the security of our scheme, which achieves selective security against offline keyword guessing attacks and guarantees the unforgeability of signatures. The comparison of experimental results illustrates that our scheme is effective and enjoys superior functionalities than the most relevant solutions.