Dynamic Bandwidth Research Articles

Dynamic wavelength and bandwidth allocation using service class prioritization for upstream in 100 Gb/s NG-EPON

A passive optical network (PON) is an auspicious access technology that keeps track of emerging application diversity. When it comes to the point of handling multiple service types together in the same network with resource limitation, there comes the important term of prioritization as all of the services do not need the same quality of service. In this paper, an efficient resource allocation scheme based on service prioritization is proposed for 100 Gb/s next-generation Ethernet passive optical network (NG-EPON) upstream. Higher-priority service quality is ensured despite load fluctuation. The proposed prioritization scheme by accessing the multiple wavelengths for higher-priority service classes also enhances bandwidth utilization. Higher-priority classes can work suitably using excess bandwidth occupied by the low-priority services during increasing demand. Further increment can preempt low-priority bandwidth for higher-priority services. We performed extensive simulation and got that at 11 % overloaded network case, the highest priority service delay of 379 msec decreased to 5 msec from the non-priority scheme to the priority scheme. The jitter of 1.06 μsec decreased to 0.035 μsec. This prioritization does not decrease the bandwidth utilization which was found near 99.26 % excluding the overhead. The proposed scheme achieves stable performance of the higher-priority service classes.

Network Traffic Prediction Performance Using LSTM

As networks expand to support various applications involving text, audio, video, and images, data traffic increases correspondingly. Traffic classification, which identifies the origin of observed traffic, has multiple applications, including dynamic bandwidth allocation, traffic analysis, quality of service, and network security. Traditional network traffic classification methods like deep packet inspection rely on manually creating and maintaining communication profiles for various applications. However, these methods face challenges such as dynamic port changes and encrypted traffic. Machine Learning (ML) classifiers offer effective solutions to these issues, providing accurate network traffic classification. Due to these advancements, deep learning models are now utilized for network traffic classification and prediction. Long Short-Term Memory (LSTM) has emerged as a highly effective deep learning technique for addressing time series prediction challenges. This study aims to analyze the performance of forecasting network traffic using LSTM, with different activation functions and optimizers with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R-Squared) parameters as the model evaluation index. This demonstrates how these parameters impact network traffic forecasting performance.

Dynamic priority-based bandwidth allocation scheme for machine type communications

The burgeoning realm of Machine Type Communications (MTC) has ushered in a paradigm shift in wireless communication, necessitating innovative strategies to efficiently manage diverse application requirements. This paper presents a pioneering Dynamic Priority-based Bandwidth Allocation (DPBA) scheme tailored specifically for MTC scenarios, encompassing both MTC and Human Type Communications (HTC) coexistence. DPBA employs an adaptive framework addressing dynamic MTC traffic, optimizing resource use while meeting latency constraints and sporadic data patterns. By dynamically prioritizing MTC applications based on requirements, the scheme allocates bandwidth for reliable data delivery. DPBA extends to MTC and HTC coexistence, managing diverse quality demands. This underlines its versatility and capacity to serve distinct needs in a shared spectrum. To substantiate its performance, the DPBA scheme is rigorously evaluated against a spectrum of related allocation methods like Proportional Fairness (PF), Static Priority Scheduling (SPS), and the Machine Learning-based Scheme (MLS), DPBA excels in simulations, minimizing packet loss, latency, enhancing throughput and ensuring fairness, surpassing benchmarks. This research underscores the critical importance of tailored bandwidth allocation strategies in advancing MTC and HTC coexistent environments. With its adaptability, efficiency, and remarkable performance, the DPBA scheme emerges as a cornerstone in the trajectory of wireless communication networks, catering to the distinct requirements of both MTC and HTC applications.

BBR-R: Improving BBR performance in multi-flow competition scenarios

The development of network infrastructures and the evolving demands of internet services impose higher requirements on congestion control algorithms. Although Google’s BBR algorithm achieves lower latency and higher goodput compared to traditional congestion control algorithms, it still has many issues. BBR sets the congestion window larger than the calculated ideal value to prevent transmission stalling in the presence of delayed and aggregated ACKs. However, in scenarios with multi-flow competition, this compromise on the congestion window leads to large amounts of queued data, causing increased latency and decreased fairness. Additionally, the ProbeRTT mechanism deviates from its original intent. In this study, we analyze the existing issues of the BBR algorithm from a theoretical standpoint and propose the BBR-R algorithm, which incorporates an adaptive sending rate adjustment mechanism and a new ProbeRTT triggering mechanism. While maintaining the ability for dynamic bandwidth exploration, the sending rate is adjusted based on a latency-related factor called Adaptive_RTprop to control the over-injected data. Coupled with the new ProbeRTT triggering mechanism, BBR-R reduces the frequency of entering the ProbeRTT phase and thereby improves transmission stability. In conducted experiments, BBR-R decreases the frequency of entering the ProbeRTT phase in many scenarios, achieves a 41.86% reduction in latency in the dual-flow competition scenario, and improves fairness by 22.79% in the five-flow competition scenario.

Geometrically nonlinear design of a rhombus-nested compliant amplification mechanism for use in precision actuators and sensors

A rhombus-nested compliant amplification mechanism is proposed for versatile usages of precision actuators and force sensors with an easy tuning of stiffness. Such a monolithically planar rhombus-nested compliant mechanism has the dual functions of two-stage displacement or force amplification by changing the input and output ports. It features a large ratio of inter-stage stiffness, thus resulting in an enhanced amplification ratio, load capacity and dynamic bandwidth. The geometrically nonlinear analytical equations of displacement amplification ratio and input stiffness are derived in the presence of pronounced axially-loaded stiffening and kinematic-arching effects based on the beam constraint model. It allows an insightful evaluation of geometrically nonlinear deformation behaviors sensitive to structural dimensions in a parametric way. Insights into geometrically nonlinear behaviors in the case of large-stroke and axially-loaded motions are discussed as well. A proof-of-concept prototype with embedded piezoelectric stacks is fabricated with the dimensions of 74mm × 60mm × 10 mm. The dual functions of precision actuator with amplified motion strokes and force sensor with enhanced sensitivity are experimentally demonstrated.

A 2 : Towards Accelerator Level Parallelism for Autonomous Micromobility Systems

Autonomous micromobility systems (AMS) such as low-speed minicabs and robots are thriving. In AMS, multiple Deep Neural Networks execute in parallel on heterogeneous AI accelerators. An emerging paradigm called Accelerator Level Parallelism (ALP) suggests managing accelerators holistically. However, there lacks a specialized and practical solution populating ALP for an AMS, where the varying real-time requirements under different working scenarios bring an opportunity to dynamically tradeoff between latency and efficiency. Furthermore, accelerator heterogeneity introduces enormous configuration space, and the shared-memory architecture results in dynamic bandwidth interference. In this paper, we propose A 2 , a novel AMS resource manager optimizing energy and memory space efficiency under variable latency constraints. We gain insight from prior Learn&Control scheme to design an Analyze&Adapt scheme specialized for heterogeneous AI accelerators under shared-memory architecture. It features analyzing the system thoroughly offline to support two-step adaptation online. We build a prototype of A 2 and evaluate it on a commercial edge platform. We show that A 2 achieves 32.8% improvements in power and 13.8% in memory compared with control-based methods. As for timeliness enhancement, A 2 reduces the deadline violation rate by 9.2 percentage points (12.8% → 3.6%) on average compared to directly porting Learn&Control methods.

Lossless Contention Ultraviolet MAC Protocol Based on Dynamic Bandwidth Allocation

Lossless Contention Ultraviolet MAC Protocol Based on Dynamic Bandwidth Allocation

Dynamic Bandwidth Slicing in Passive Optical Networks to Empower Federated Learning.

Federated Learning (FL) is a decentralized machine learning method in which individual devices compute local models based on their data. In FL, devices periodically share newly trained updates with the central server, rather than submitting their raw data. The key characteristics of FL, including on-device training and aggregation, make it interesting for many communication domains. Moreover, the potential of new systems facilitating FL in sixth generation (6G) enabled Passive Optical Networks (PON), presents a promising opportunity for integration within this domain. This article focuses on the interaction between FL and PON, exploring approaches for effective bandwidth management, particularly in addressing the complexity introduced by FL traffic. In the PON standard, advanced bandwidth management is proposed by allocating multiple upstream grants utilizing the Dynamic Bandwidth Allocation (DBA) algorithm to be allocated for an Optical Network Unit (ONU). However, there is a lack of research on studying the utilization of multiple grant allocation. In this paper, we address this limitation by introducing a novel DBA approach that efficiently allocates PON bandwidth for FL traffic generation and demonstrates how multiple grants can benefit from the enhanced capacity of implementing PON in carrying out FL flows. Simulations conducted in this study show that the proposed solution outperforms state-of-the-art solutions in several network performance metrics, particularly in reducing upstream delay. This improvement holds great promise for enabling real-time data-intensive services that will be key components of 6G environments. Furthermore, our discussion outlines the potential for the integration of FL and PON as an operational reality capable of supporting 6G networking.

Dynamic Bandwidth Allocation for Collaborative Multi-Robot Systems Based on Task Execution Measures

Multi-robot systems (MRSs) is a growing field of research that focuses on the collaboration of multiple robots to achieve a common global objective. Managing these systems poses several challenges, including coordination, task allocation, and communication. Among these challenges, a major area of focus is devising an effective communication scheme that ensures robots’ cooperation and adapts to varying conditions during task execution. In this paper, we develop a novel communication management framework tailored for MRSs, specifically addressing dynamic bandwidth distribution in networked teleoperated robotic systems. The algorithm is combined with semi-autonomous formation control based on the Artificial Potential Fields (APF) algorithm, which allows each individual robot to avoid local obstacles autonomously and tries to maintain a desired formation with its neighbors, while the operator is in charge of high-level control only. Common Dynamic Bandwidth Allocation (DBA) algorithms allocate bandwidth to different units based on network conditions and requirements. On the other hand, our proposed DBA scheme dynamically distributes the available bandwidth on communication streams based on factors related to task execution and system performance. In specific, bandwidth is allocated in a way that adapts to changes occurring in the system’s environment and its internal state, including the effect of the autonomous action taken by the path planner on the MRS and the performance of the controller of each individual robot. By addressing the limitations of existing approaches through shaping the communication behavior of the MRS based on performance measures, our proposed algorithm offers a promising solution for improving the performance and efficiency of MRSs. The proposed scheme is tested through simulations on a group of six unmanned aerial vehicles (UAVs) in the Robot Operating System (ROS)-Gazebo simulation environment. The obtained results show the scheme’s capability for enhancing the robotic system’s performance while significantly reducing bandwidth consumption. Experimental testing on two mobile robots further demonstrates the effectiveness of the proposed scheme.

Towards Millions of Database Transmission Services in the Cloud

Alibaba relies on its robust database infrastructure to facilitate realtime data access and ensure business continuity despite regional disruptions. To address these operational imperatives, Alibaba developed the Data Transmission Service (DTS), which has become critical for internal applications and public cloud services alike. This paper presents a comprehensive study of the architectural innovations, resource scheduling mechanisms, and performance optimization strategies that have been implemented within DTS to tackle the significant challenges of cross-network, heterogeneous data transmission in a cost-effective manner. We explore the novel Any-to-Any (A2A) architecture, which simplifies the complexity of data paths between diverse databases and mitigates network connectivity issues, thereby significantly reducing development overhead. Additionally, we examine a dynamic network bandwidth scheduling algorithm that effectively maintains Service-Level Objectives (SLOs), complemented by a serverless mechanism that ensures efficient resource utilization. Furthermore, DTS utilizes advanced strategies such as transaction dependency tracking, hot data consolidation, and batching to enhance synchronization performance and efficiency. DTS has distilled the lessons learned from years of serving our customer base and currently supports nearly 1 million public cloud instances annually. Our evaluation results show that DTS can effectively and efficiently handle real-time data transmission in both experimental and production environments.

Event-Triggered Collaborative Fault Diagnosis for UAV–UGV Systems

The heterogeneous unmanned system, which is composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV), has been broadly applied in many domains. Collaborative fault diagnosis (CFD) among UAVs and UGVs has become a key technology in these unmanned systems. However, collaborative fault diagnosis in unmanned systems faces the challenges of the dynamic environment and limited communication bandwidth. This paper proposes an event-triggered collaborative fault diagnosis framework for the UAV–UGV system. The framework aims to achieve autonomous fault monitoring and cooperative diagnosis among unmanned systems, thus enhancing system security and reliability. Firstly, we propose a fault trigger mechanism based on broad learning systems (BLS), which utilizes sensor data to accurately detect and identify faults. Then, under the dynamic event triggering mechanism, the network communication topology between the UAV–UGV system and BLS is used to achieve cooperative fault diagnosis. To validate the effectiveness of our proposed scheme, we conduct experiments on a software-in-the-loop (SIL) simulation platform. The experimental results demonstrate that our method achieves high diagnosis accuracy for the UAV–UGV system.

Research on the Fiber-to-the-Room Network Traffic Prediction Method Based on Crested Porcupine Optimizer Optimization

In order to solve the problem of traffic burst due to the increase in access points and user movement in an FTTR network, as well as to meet the demand for a high-performance network, it is necessary to rationally allocate network resources, and accurate traffic prediction is very important for dynamic bandwidth allocation in such a network. Therefore, this paper introduces a novel traffic prediction model, named CPO-BiTCN-BiLSTM-SA, which integrates the Crested Porcupine Optimizer (CPO), bidirectional temporal convolution (BiTCN), and bidirectional long short-term memory (BiLSTM) networks. BiTCN extends the traditional TCN by incorporating bidirectional data information, while BiLSTM enhances the network’s capability to learn from long sequences. Moreover, self-attention (SA) mechanisms are utilized to emphasize the crucial segments in the data. Subsequently, the BiTCN-BiLSTM-SA model is optimized by CPO to obtain the best network hyperparameters, and model training prediction is performed to achieve multi-step predictions based on single-step prediction. To evaluate the model’s generalization ability, two distinct datasets are employed for traffic prediction. Experimental findings demonstrate that the proposed model surpasses existing models in terms of the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In comparison with the traditional XGBoost model, the proposed model has an average reduction of 29.50%, 25.43%, and 25.00% in RMSE, MAE, and MAPE, respectively, with a 6.70% improvement in R2.

Orchid: enhancing HPC interconnection networks through infrequent topology reconfiguration

Interconnection networks are key components of high-performance computing (HPC) systems. As HPC evolves towards the exascale era, providing sufficient bisection bandwidth between computing node pairs through oversubscription in traditional networks becomes prohibitively expensive and impractical. Over the past decade, several architectures leveraging optical circuit switches (OCSs) for dynamic link bandwidth allocation have gained traction. These architectures require frequent network topology reconfiguration to adapt to changing traffic demands. However, practical implementation remains hampered by the long reconfiguration delays inherent in OCS technology. We propose Orchid, an architecture that leverages OCSs to achieve infrequent topology reconfigurations, effectively addressing the problem of long reconfiguration delays. A key innovation of Orchid is its ability to extract stable traffic matrices from historical data. This functionality guides the reconfiguration of the topology without the need for adjustments with each traffic matrix, thereby enabling the sharing of OCS overhead over an extended timeframe. Furthermore, Orchid addresses potential congestion arising from unexpected traffic through the joint design of OCS configuration and routing, ensuring an even distribution of traffic across global links. Extensive experiments using real HPC application traces and synthetic traffic demonstrate that Orchid achieves significant performance improvements compared to existing HPC interconnection networks. Specifically, Orchid reduces packet delay by at least 3× and enhances throughput by up to 60%.

Multiplexable all-optical nonlinear activator for optical computing

As an alternative solution to surpass electronic neural networks, optical neural networks (ONNs) offer significant advantages in terms of energy consumption and computing speed. Despite the optical hardware platform could provide an efficient approach to realizing neural network algorithms than traditional hardware, the lack of optical nonlinearity limits the development of ONNs. Here, we proposed and experimentally demonstrated an all-optical nonlinear activator based on the stimulated Brillouin scattering (SBS). Utilizing the exceptional carrier dynamics of SBS, our activator supports two types of nonlinear functions, saturable absorption and rectified linear unit (Relu) models. Moreover, the proposed activator exhibits large dynamic response bandwidth (∼11.24 GHz), low nonlinear threshold (∼2.29 mW), high stability, and wavelength division multiplexing identities. These features have potential advantages for the physical realization of optical nonlinearities. As a proof of concept, we verify the performance of the proposed activator as an ONN nonlinear mapping unit via numerical simulations. Simulation shows that our approach achieves comparable performance to the activation functions commonly used in computers. The proposed approach provides support for the realization of all-optical neural networks.

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.

Cyber–physical system architecture of autonomous robot ecosystem for industrial asset monitoring

Driven by advancements in Industry 4.0, the Internet of Things (IoT), digital twins (DT), and cyber–physical systems (CPS), there is a growing interest in the digitalizing of asset integrity management. CPS, in particular, is a pivotal technology for the development of intelligent and interconnected systems. The design of a scalable, low-latency communication network with efficient data management is crucial for connecting physical and digital twins in heterogeneous robot fleets. This paper introduces a generalized cyber–physical architecture aimed at governing an autonomous multi-robot ecosystem via a scalable communication network. The objective is to ensure accurate and near-real-time perception of the remote environment by digital twins during robot missions. Our approach integrates techniques such as downsampling, compression, and dynamic bandwidth management to facilitate effective communication and cooperative inspection missions. This allow for efficient bi-directional data exchange between digital and physical twins, thereby enhancing the overall performance of the system.This study contributes to the ongoing research on the deployment of cyber–physical systems for heterogeneous multi-robot fleets in remote inspection missions. The feasibility of the approach has been demonstrated through simulations in a representative environment. In these experiments, a fleet of robots is used to map an unknown building and generate a common 3D probabilistic voxel-grid map, while evaluating and managing bandwidth requirements. This study represents a step forward towards the practical implementation of continuous remote inspection with multi-robot systems through cyber–physical infrastructure. It offers potential improvements in scalability, interoperability, and performance for industrial asset monitoring.

Dynamic Trajectory Design and Bandwidth Adjustment for Energy-Efficient UAV-Assisted Relaying With Deep Reinforcement Learning in MEC IoT System

Dynamic Trajectory Design and Bandwidth Adjustment for Energy-Efficient UAV-Assisted Relaying With Deep Reinforcement Learning in MEC IoT System

Dynamic Bitrate Adaptation and Bandwidth Allocation for MEC-Enabled Video Streaming

Dynamic Bitrate Adaptation and Bandwidth Allocation for MEC-Enabled Video Streaming

Deep Reinforcement Learning for Scalable Dynamic Bandwidth Allocation in RAN Slicing With Highly Mobile Users

Deep Reinforcement Learning for Scalable Dynamic Bandwidth Allocation in RAN Slicing With Highly Mobile Users