Bandwidth Allocation Research Articles

Task offloading method based on CNN-LSTM-attention for cloud–edge–end collaboration system

Most of the existing task offloading methods in edge computing environments do not fully utilize the processing capabilities of cloud servers and have high computational complexity, making them unsuitable for real-time processing tasks. To address this problem, we propose a cloud–edge–end collaborative task offloading method based on deep learning methods, combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and attention mechanisms. This method models the total cost of the cloud–edge–end collaboration system as a weighted sum function of the time delay and energy consumption of task execution. Then, with the objective of minimizing the total cost of the system, the task offloading problem is formulated as a mixed-integer joint optimization problem with offloading decision and bandwidth allocation, and two subproblems are decomposed from the optimization problem: one focuses on offloading decision and the other on bandwidth allocation. A CNN-LSTM-Attention neural network-based method is proposed to solve the optimal offloading decision efficiently. Based on this, the differential evolution algorithm is used to generate the optimal bandwidth allocation, resulting in efficient task offloading. The simulation experiment results demonstrate that our method enhances system performance and reduces the overall cost of the system, which is significantly better than existing methods.

Time-triggered scheduling of mixed-critical flows at end-system in asynchronous AFDX avionic network

Avionics Full-DupleX (AFDX) is a switched Ethernet-based network used in modern commercial airplanes for the transmission of command and control avionics flows. These critical flows require deterministic guarantees leading to a lightly loaded network. Aircraft manufacturers envision to carry additional non avionics flows (i.e. video, audio, service) to take advantage of the spare bandwidth. However, it is then compulsory to preserve the real-time guarantees of avionics flows in terms of bounded jitter at the transmitter output and of bounded end-to-end latency. Depending on the type of additional traffic, Quality of Service (QoS) end-to-end guarantees may be offered to the additional flows of lower criticality in terms of reduced delay or bounded jitter for instance. These guarantees can be ensured by scheduling policies at transmitter and switch level. However, an important safety-related constraint is the asynchronous design of avionics distributed systems that prohibits the use of a network-wide synchronization of end systems and switches. Thus, time-triggered networking such as emerging Time-Triggered Ethernet (TTEthernet) or Time-Sensitive Networking (TSN) cannot be leveraged. This paper underlines the benefits of only scheduling flows at the transmitter, which is compatible with the asynchronous safety constraint of avionics systems. We show that it is possible to build a table schedule that carries both critical avionics flows and additional video flows that meet their timing and bandwidth allocation constraints. Therefore, we design scheduling strategies for efficient distribution of flows in the table scheduling whose performance is compared to the optimal schedule minimizing the emission lag of additional flows. Proposed heuristics are constructed such as to favor the network responsiveness for avionics flows thanks to slot over-provisioning. Extensive results on an A350 AFDX industrial configuration show that for a transmitter load of up to 70% of the available bandwidth, the uniform allocation heuristic provides a performance close to optimal in terms of minimum emission lag for additional flows, so offering a practical configuration heuristic to industry.

Partial and cost-minimized computation offloading in hybrid edge and cloud systems

Nowadays, numerous mobile devices (MDs) provide nearly anytime and anywhere services, running on top of various computation-intensive applications. However, bearing limited battery, bandwidth, computing, and storage resources, MDs cannot completely execute all tasks of such applications in real-time. Cloud data centers (CDCs) possess enormous resources and energy, which can help execute tasks offloaded from MDs. Nonetheless, CDCs reside in remote sites, thereby leading to long transmission latency. In recent years, small base stations (SBSs) have emerged to offer close proximity, high bandwidth, and low latency services to their nearby and limited MDs. However, it becomes a new challenge to minimize the total system cost in a complex and heterogeneous architecture. To address it, this work proposes an energy-minimized partial computation offloading technique. First, a limited optimization problem of cost minimization for the system is formulated. Afterward, an improved hybrid meta-heuristic algorithm is developed, which synergistically combines a Metropolis acceptance criterion of simulated annealing and genetic operations. One uniqueness of the proposed algorithm is that it simultaneously determines task allocation among MDs, an SBS, and a CDC, the transmission power of MDs, and bandwidth allocation of wireless channels between MDs and SBS. Experiments with real-life tasks from Google data centers have shown that the proposed Genetic Simulated-annealing-based Particle swarm optimization (GSP) significantly achieves lower system cost and faster convergence speed than benchmark peers.

Dynamic event-triggered state estimation for discrete-time delayed switched neural networks with constrained bit rate

In this paper, a class of discrete-time delayed switched neural networks with dynamic event-triggered mechanism (DETM) and constrained bit rate is considered. In order to reduce the transmission frequency and alleviate the unnecessary resource loss between sensor and estimator, a DETM is proposed. The data transmission from sensor to estimator is realized through constrained bit rate channel. Therefore, in order to reflect the bandwidth allocation rules of accessible neurone nodes, a bit rate constraint model is introduced and an encoding-decoding mechanism is developed. This paper is concerned with the strategy of average dwell time (ADT) and linear matrix inequality, then sufficient conditions for the exponential ultimate boundedness of switched neural networks with DETM and constrained bit rate are proposed. Finally, an example is given to prove the effectiveness of the results.

A cloud-edge collaborative task scheduling method based on model segmentation

With the continuous development and combined application of cloud computing and artificial intelligence, some new methods have emerged to reduce task execution time for training neural network models in a cloud-edge collaborative environment. The most attractive method is neural network model segmentation. However, many factors affect the segmentation point, such as resource allocation, system energy consumption, load balancing, and network Bandwidth allocation. Some segmentation methods consider the shortest task execution time, which ignores the utilization of resources at the edge and can result in resource waste. Additionally, these factors are difficult to measure, which presents a challenge in calculating the best segmentation point to achieve the goal of maximum resource utilization and minimum task execution time. To solve this problem, this paper proposes a cloud-edge collaborative task scheduling method based on model segmentation (CECMS). This method first analyzes the factors affecting the segmentation point of the model and then obtains accurate factors that affect the segmentation point calculation through the pre-execution method. Furthermore, a multi-objective solution algorithm is improved to calculate the optimal model segmentation point. And tasks are separately offloaded to the edge and cloud based on the optimal model segmentation point. Finally, the experiments are conducted to verify the effectiveness of this method. Finally, the effectiveness of the CECMS method was verified through simulation experiments. Compared with the Dynamic Adaptive DNN Surgery (DADS) method and an adaptive DNN inference acceleration framework algorithm with end–edge–cloud collaborative computing algorithm (ADC), CECMS achieves the same effectiveness as DADS and ADC in optimizing task execution time by comprehensively considering the utilization of edge resources and minimizing task execution time, while also effectively ensuring resource utilization.

Adaptive quality of service for packet loss reduction using OpenFlow meters.

Quality of Service (QoS) is a mechanism used in computer networks to prioritize, classify, and treat packets differently based on certain criteria. This helps the switching devices to schedule and reorder packets if there is congestion in the network. Edge routers experience high traffic congestion as a result of traffic aggregation from the internal network devices. A router can have multiple QoS classes configured, and each class could experience traffic at various rates. However, when a QoS class is underperforming or needs more bandwidth, some bandwidth can be borrowed or leased out to another QoS class to ensure the link is utilized to maximum capacity and the highest throughput is achieved. This article proposes a bandwidth allocation and distribution algorithm that purely uses the flow statistics from the OpenFlow switches to allocate bandwidth to different QoS classes optimally based on their current requirement. The algorithm does not guarantee in advance that the packet loss will be minimized but does guarantee the initial minimum bandwidth allocation. It adjusts the flows' rates with the aim to increase their current throughput. The algorithm uses the Software Defined Networking (SDN) controller's flow monitoring component to query the flow statistics from the switch to first approximate the traffic flow rate and then calculate the optimal bandwidth values to assign to each QoS class. The proposed algorithms will be applied to certain switches in the path with the assumption that all the switches are OpenFlow compatible. The algorithm's performance was compared with the Adaptive Quality of Service (AQoS) algorithm over various traffic scenarios. The results show that the proposed algorithm achieves an average of 9% performance gain compared to the AQoS algorithm.

Architecture for low-cost and highly flexible metro-access networks using SOA-based OADM nodes and digital subcarrier multiplexing with power loading.

Metro-access networks exploiting wavelength division multiplexing (WDM) to cope with the ever-growing bandwidth demands are sensitive to cost and need to be fast-configurable to meet the requirements of many new network services. Optical add-drop multiplexers (OADMs) are a key component in enabling fast dynamic wavelength allocation and optimization. In this Letter, we propose and demonstrate, to our knowledge, a novel architecture for high-performance metro-access networks that utilizes semiconductor optical amplifier (SOA)-based OADM nodes, digital subcarrier multiplexing (DSCM), low-cost direct detection receivers, and power loading techniques, which makes the designed metro-access network cost-effective, fast reconfigurable, and flexible for bandwidth allocation on demand. Through a proof-of-concept experiment, we have successfully demonstrated a prototype horseshoe optical network consisting of up to four SOA-based OADM nodes at 40 Gb/s per wavelength channel by leveraging the proposed scheme. The flexible bandwidth allocation and dynamic add and drop operations have also been achieved in an emulated WDM optical network. All results indicate the great scalability and flexibility of the proposed architecture.

A comprehensive survey of performance, security and privacy issues in the network interface layer of the TCP/IP

The network interface layer of the TCP/IP protocol suite, primarily comprised of the Internet Protocol (IP), serves as the backbone of modern internet communication. With its efficient data delivery, The network interface layer, presents key challenges in terms of performance, security, and privacy. This comprehensive survey delves into these three crucial aspects, analyzing the inherent vulnerabilities, limitations of the interface layer, and provide solutions of the related problems. The performance analysis explores throughput, latency, and bandwidth constraints, along with solutions such as bandwidth allocation and optimization techniques. Vulnerabilities within Network Interface Layer, including denial-of-service attacks and MAC address spoofing, are discussed, along with a review of existing security mechanisms. Privacy flaws are examined, covering MAC address tracking, profiling risks, and anonymization techniques, while also addressing privacy considerations on the Internet of Things. The survey analyzes several case studies providing comparative analysis of the network interface layer protocols, with support of the real world scenarios including performance analysis in high density environment, and security and privacy risks in smart homes networks. The findings provide a comprehensive understanding of the complexities surrounding performance, security, and privacy issues future directions and potential solutions.

L-band of ∼ 1.6 μm tunable multi-wavelength mode-locked Er-doped fiber laser with an MMF- FMF based structure

1.6 μm tunable mode-locked fiber laser are mainly used in the biomedical field, laser processing, and other aspects, have excellent application prospects. We propose a novel MMF-FMF-based device, which acts both as a saturable absorber and a wavelength selection filter. The reduction of insertion loss caused by FMF affects the laser gain competition process, leading to changes in gain allocation and bandwidth allocation. The laser generated stable mode-locked pulses with a repetition frequency of 23.23 MHz at pump powers exceeding 51 mW. By adjusting the angle of the fiber optic squeezer with the PC, successfully achieved switchable single-, dual-, and triple-wavelength lasing outputs, as well as tunable single- and dual-wavelength lasing outputs. The wavelength-tuning ranges for the single- and dual-wavelength lasing outputs were 31 nm (1570 nm-1601 nm) and 24 nm, respectively. The accurately adjustable L-band novel mode-locked fiber laser exhibits enormous potential for application.

Shapley’s Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM

The hybrid in-band on-channel (IBOC) transmission system, which serves as a digital audio radio scheme, facilitates the simultaneous transmission of analog FM and digital audio. In IBOC systems, broadcasters transmit signals within allocated channel bands, posing a challenge in bandwidth allocation for digital audio transmission. To address this, cooperative game theory, supported by the bankruptcy game and Shapley’s value, is proposed as a strategy to optimize bandwidth allocation at each node or station. This approach considers service demand, the number of stations, and radio channel conditions in the FM band. The paper presents a scenario under saturated traffic conditions to evaluate the degree of optimization achievable using the Shapley value under clearly defined traffic and channel conditions. The results indicate that cooperative game theory, with the support of the Shapley value, offers an excellent alternative for optimizing bandwidth in digital broadcasting over IBOC in FM, with potential applicability in other digital radio broadcasting systems.

Assessment of Communication Resource Allocation by the Transmission Control Protocol for the Target Virtual Connection under Competitive Conditions

The mathematical framework presented in this article focuses on the controlled-transmission protocol’s asynchronous process of bandwidth allocation for the target virtual connection implemented under competition for communication resources. The studied process is formalized as a two-dimensional discrete Markovian chain, taking into account the distributions of queue lengths of TCP data fragments from competing client nodes. Such a chain describes the dynamics of filling the stack of transmitted but unacknowledged data fragments of the investigated end device. Distributions of the chain states were found for various ratios of the target virtual-connection bandwidth, transmission-protocol parameters, and communication-channel characteristics. Analytical dependencies for computing the performance of the target virtual connection for different operating modes were obtained. The results of experiments conducted based on the obtained analytical constructions showed that the performance of the virtual connection with a selective repeat mode is mainly determined by the data-loss intensity, the queue size distribution in transit nodes, and the ratio between the protocol window size and the route length.

A deep learning based dynamic bandwidth allocation method for XG-PON based mobile fronthaul for CRAN

In the context of 5G and beyond era, the use of Time Division Multiplexed Passive Optical Network (TDM-PON) for mobile fronthaul (MFH) in centralized/cloud radio access network (CRAN) has proven to be an optimal solution for addressing low-latency requirements. While existing literature has predominantly focused on optimizing mobile fronthaul latency rather than end-to-end latency, this paper introduces an end-to-end latency model for TDM-PON, specifically within a 10-gigabit Passive Optical Network (XG-PON) based MFH for CRAN. The paper then proposes an Intelligent Dynamic Bandwidth Allocation (DBA) scheme to minimize end-to-end latency of the network. The proposed scheme predicts the buffer occupancy reports using deep learning techniques at optical network units (ONUs). Thereafter, optical line terminal (OLT) schedules the available bandwidth using conventional DBA schemes (Group-GIANT i.e., Ggiant, Optimized Round Robin, Dynamic Service Interval). Primarily, the proposed DBA scheme transforms conventional DBA schemes (Ggiant, Optimized Round Robin, Dynamic Service Interval) into Intelligent versions (Intelligent Ggiant, Intelligent ORR, Intelligent DSI), showcasing a reduction of 28.63 %, 45.86 %, and 48.60 % in end-to-end latency in the XG-PON-based MFH for CRAN. Further, the analysis of obtained results has confirmed the supremacy of the Intelligent DSI DBA scheme over the Intelligent Ggiant and the Intelligent ORR DBA scheme.

An efficient deep learning mechanisms for IoT/Non-IoT devices classification and attack detection in SDN-enabled smart environment

In recent years, the development of Internet of Things (IoT) applications has increased, resulting in higher demands for sufficient bandwidth, data rates, latency, and quality of service (QoS). In advanced communications, managing network resources for allocating IoT services and identifying the exact IoT devices connected to a network is a major concern. The existing studies have introduced various methods for classifying IoT devices in a network. However, the previous studies faced challenges like limited attributes, low efficiency, inappropriate features, and computational complexities. Also, the existing studies failed to concentrate on IoT/Non-IoT classification along with attack detection. Detecting attacks on IoT devices is critical for making network services more effective. Thus, the proposed study introduces an efficient IoT device classification and attack detection mechanism using software defined networking (SDN)-enabled fiber-wireless access networks internet of things (FiWi IoT) architecture. Initially, an effective resource allocation process is performed to mitigate the delay constraint issues by introducing a hybrid parallel neural network-based dynamic bandwidth allocation (DBA) method. Then, the input traffic information is gathered from the resource-efficient SDN-enabled FiWi IoT network, and the input data is pre-processed to eliminate unwanted noises using min-max normalization and standardization. Next, the essential attributes are extracted to attain enhanced classification performance. To reduce the feature dimensionality problem and thereby solve complexity issues, the most optimal features are selected by a new chaotic seagull optimization (CSO) approach. After that, IoT/non-IoT classification is performed using a transformer-driven deep intelligent model. Finally, the attacks are detected and classified by introducing a novel slice attention-based deep capsule autoencoder (SA_DCAE) model. For experimentation, the Python 3.7.0 tool is used in this work, and the performance of proposed classifiers is measured by evaluating varied matrices. Also, the comparison analysis proves the superiority of the proposed techniques to other existing methods.

A Systematic Review on Non-Orthogonal Multiple Access (NOMA) Based on Visible Light Communication for Intelligent Transportation Systems

Non-Orthogonal Multiple Access (NOMA) is a cutting-edge technology that permits several users to use the same frequency resources and transfer data at once. Visible light communication also known as VLC, is an innovative technology that offers access to an unoccupied spectrum, enables fast data transmission speeds, thereby making it a complementary addition to current radio frequency technology. Although VLC has its advantages, it also comes with some downsides. One major issue is the modulation bandwidth of LEDs, which can be quite challenging when creating high speed VLC systems. Non-orthogonal multiple access (NOMA), particularly power-domain NOMA (PD-NOMA), has become a viable multiple access approach, ensuring sufficient bandwidth allocation for VLC systems. This study delves into multiple access techniques in VLC systems, in particular NOMA, and discusses its fundamental principles, key ideas and practical implementations like vehicular communication. It also addresses the associated hurdles, such as ensuring equitable distribution of optical power among users, leveraging MIMO technology, and grappling with LED nonlinearity issues. In summary, we underscore the significance of utilizing NOMA-VLC technology to enhance wireless communication systems and optimize spectral efficiency.

Client scheduling and bandwidth slicing for multiple federated learning tasks over multiple passive optical networks

Federated Learning (FL) has attracted extensive attention in facilitating emerging edge intelligence applications for its inherent advantages of ensuring data security and privacy. Especially in the edge computing networks connected by Passive Optical Network (PON) system, FL is introduced to enable applications like autonomous driving, intelligent manufacturing, and precision medicine. However, in this system, the FL deployment over PON inevitably faces challenges induced by the conflict between a large volume of model data with the restrict latency limitation and the confined bandwidth resource of PON, especially for the multiple FL tasks. To address these issues, a novel scheme is proposed for tackling the client distribution and bandwidth allocation problems under the scenario of multiple simultaneous FL tasks supported by the multiple interconnected PON systems, which is consisted of the client scheduling and bandwidth slicing processes. To be specific, an easy-to-implement heuristic algorithm is first performed to assign the client numbers to PONs based on iterative method, with which certain operations are repetitively executed to achieve optimal solutions. And then, the serial bandwidth slicing which adapts the traditional policy, i.e., one-task-per-cycle, to the situation with multiple FL tasks, and parallel slicing with the multi-task-per-cycle, are designed for the investigated system. Furthermore, the simulation system is constructed to verify our method. The corresponding results exhibit that, the largest 43.2 % round time reduction is achieved by client scheduling compared to benchmark without the scheduling. Compared to the benchmark with serial slicing, our scheme can achieve a maximum 50.11 % of the round time decrease. It's also validated that, our proposed client scheduling and parallel bandwidth slicing method can improve the learning efficiency by reducing communication delay, especially for the situation with less client number and smaller FL threshold.

WiMAX Network Optimization Using Frame Period with Channel Allocation Techniques

Internet connectivity in WiMAX networks, along with various applications, is increasing rapidly, so the connectivity of internet and data transfer speed are always challenge for effective data transmission in wireless networks. Several factors affect the performance of networks. One important factor is to choose a suitable frame period for effective data transmissions. The performance of different frame period with round-robin and strict priority are evaluated in this work. A frame period in round robin performs better than a strict priority in terms of throughput, but a strict priority performs better in terms of drop rates is observed. This paper also demonstrates that an effective frame period, when combined with a proper bandwidth allocation algorithm, yields better results. This work gives the analysis that round robin performs 83.8847% better while strict priority performs 86.0020% better than the earliest deadline first algorithms for 10 subscriber stations in terms of throughput. This work is helpful to researchers and industrialists for actual implementations in WiMAX networks.

Solution of the Simultaneous Routing and Bandwidth Allocation Problem in Energy-Aware Networks Using Augmented Lagrangian-Based Algorithms and Decomposition

We discuss several algorithms for solving a network optimization problem of simultaneous routing and bandwidth allocation in green networks in a decomposed way, based on the augmented Lagrangian. The problem is difficult due to the nonconvexity caused by binary routing variables. The chosen algorithms, which are several versions of the Multiplier Method, including the Alternating Direction Method of Multipliers (ADMM), have been implemented in Python and tested on several networks’ data. We derive theoretical formulations for the inequality constraints of the Bertsekas, Tatjewski and SALA methods, formulated originally for problems with equality constraints. We also introduce some modifications to the Bertsekas and Tatjewski methods, without which they do not work for an MINLP problem. The final comparison of the performance of these algorithms shows a significant advantage of the augmented Lagrangian algorithms, using decomposition for big problems. In our particular case of the simultaneous routing and bandwidth allocation problem, these algorithms seem to be the best choice.

Cooperative sensing, communication and computation resource allocation in mobile edge computing-enabled vehicular networks

The combination of integrated sensing and communication (ISAC) with mobile edge computing (MEC) enhances the overall safety and efficiency for vehicle to everything (V2X) system. However, existing works have not considered the potential impacts on base station (BS) sensing performance when users offload their computational tasks via uplink. This could leave insufficient resources allocated to the sensing tasks, resulting in low sensing performance. To address this issue, we propose a cooperative power, bandwidth and computation resource allocation (RA) scheme in this paper, maximizing the overall utility of Cramér-Rao bound (CRB) for sensing accuracy, computation latency for processing sensing information, and communication and computation latency for computational tasks. To solve the RA problem, a twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to explore and obtain the effective solution of the RA problem. Furthermore, we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks, as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations. Simulation demonstrates that compared to other benchmark methods, TD3 achieves an average utility improvement of 97.11% and 27.90% in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.

Integration of speed control and bandwidth allocation to enable fair uplink communications in UAV-aided aerial base stations based on Open-RAN

Integration of speed control and bandwidth allocation to enable fair uplink communications in UAV-aided aerial base stations based on Open-RAN

Efficient Approximation Methods for Lexicographic Max-Min Optimization

Lexicographic max-min (LMM) optimization is of considerable importance in many fairness-oriented applications. LMM problems can be reformulated in a way that allows to solve them by applying the standard lexicographic maximization algorithm. However, the reformulation introduces a large number of auxiliary variables and linear constraints, making the process computationally complex. In this paper, two approximation schemes for such a reformulation are presented, resulting in problem size reduction and significant performance gains. Their influence on the quality of the solution is shown in a series of computational experiments concerned with the fair network dimensioning and bandwidth allocation problem.