Dynamic Bandwidth Allocation 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.

Design of an Improved Method for P-cycle Protection and Dynamic Bandwidth Allocation in Elastic Optical Networks for Healthcare Applications

The acceptance of a modern-day health care system with telemedicine and remote patient monitoring requires extremely reliable and flexible network infrastructures. Thus, the need for very changeable Elastic Optical Networks, with dynamic bandwidth allocation for extremely diversified health care-oriented information, such as real-time video consultation, continuous patient monitoring, and huge medical imaging files. However, the current network infrastructures struggle very often with the optimization of reliability against resource utilization, particularly for unanticipated failures, which will bring service disruption to the healthcare services and damage patient outcomes. To address these limitations, this paper proposes an advanced network protection model using P-cycle protection in EONs to enhance network resilience by minimizing resource consumption. P-cycles refer to backup loops that are preconfigured such that, upon failure of a link, the traffic could be rerouted instantaneously. Unlike the conventionally used protection schemes, the restoration of P-cycles can occur in much faster duration with less resource loss. In this model, we propose an intelligent adaptation of P-cycles consistent with the dynamic bandwidth requirements of healthcare applications. It does manage to achieve these functionalities through the inclusion of algorithms for traffic-aware P-cycle design to optimize the capabilities of the utilized network for recovery through the least wastage of resources. There are huge implications of P-cycle protection introduced in EONs for healthcare networks. It further increases the reliability of systems for real-time patient monitoring by ensuring non-stop data transmission to decrease system latency in the event of a loss of packets caused by a network failure. The principal beneficiaries of telemedicine are uninterrupted services for high-quality video consultation as well as reliable, massive transmission of medical data in the form of MRI and CT images. This is because of the positive results that are beginning to come from health care, which is becoming more reliable and expedient, especially in rural and critical scenarios.

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.

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%.

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.

Timeliness Study of Home Energy Management System Based on Dynamic Allocation of Bandwidth

Timeliness Study of Home Energy Management System Based on Dynamic Allocation of Bandwidth

Securing Pervasive Computing Networks: Enhancing Network Security via Network Virtualization in Wireless Communications Infrastructure

The seamless integration of technology for computing into everyday items and environments is known as pervasive computing. To protect against cyber threats and vulnerabilities, robust security mechanisms are necessary. Conventional security measures, including gateways and the use of encryption, may not be sufficient to address the unique challenges encountered in ubiquitous computing systems. But these techniques are still vital. In addition to the variety of devices, resource limitations, mobility needs, and the possibility of large-scale distributed attacks, these obstacles also include the potential for attack. Network virtualization, that abstracts and separates network facilities and functions, is a promising way to increasing security in pervasive computing deployments: it abstracts and isolates network resources and processes. Wireless communication play a significant part in the development of a digital infrastructure that is both resilient and trustworthy. The processes of dynamic resource allocation, isolation, and management of network bandwidth are made possible through the utilization of virtualization, leads to the proposal of Secure Wireless Virtual Resource Allocation and Authentication Algorithm(SWVRA3) to make the abstraction of the network's physical resources into virtualized entities By using network virtualization, pervasive computing applications and services can be secured with logically segregated virtual networks. The cross-contamination and security breaches can be reduced by this separation. Furthermore, flexible configuration, dynamic allocation of resources, and centralized virtual control are allowed by network visualization that improves threat incidence response, enforcement of policies, and security surveillance.

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

An Improved Polling Mechanism for Dynamic Bandwidth Allocation of XGPON Networks

Abstract: One of the most well-known technologies that offers a large range of services at user’s fingertips without requiring the least amount of infrastructure is passive optical networks. This can be due to a particular component known as the passive optical spiller. In order to facilitate time division access for individual ONUs on a single optical fiber, the appropriate dynamic bandwidth allocation mechanism must be present at the OLT with the advent of user support. For bandwidth assignment by DBA, it is essential to determine the precious upstream bandwidth demand, and can be aided by the more frequent polling mechanism. The more frequent polling mechanism lead to erroneous bandwidth report problem, become more serious when the mechanism is employed in larger coverage area network (specifically larger channel delay). This IBR problem lead increase in upstream delay at higher load condition, it’s because of improper assignment of bandwidth and waste of bandwidth. The IBR issue is resolved by the improved polling and scheduling method presented in this paper. Instead of transmitting the bandwidth allotment in each downstream frame, the OLT uses this scheduling technique to send it all at once to a TCONT (i) of ONU (i) for the duration of the SI period. To counteract the channel delay impact, a unique polling technique is also presented that use the hybrid methodology in which the OLTs sends polling mechanism once per the Service interval but According to the simulation study’s findings, the suggested system reduces mean upstream delay for type 2, 3, and 4 traffic classes by 62% when compared to the EBU scheme and by up to 20% when compared to the XGIANT design.

Comparative analysis of passive optical networks using multiple parameters: a review

Abstract The desideratum for high-end communication networks and high-speed data transfer has surged exorbitantly in the contemporary era. To meet this demand, various optical access network technologies have been invented and assembled, including gigabit passive optical networks (GPON), Ethernet passive optical networks (EPON), and wavelength multiplexed passive optical networks (WDM-PON). Due to their huge bandwidth capacity, excellent compatibility, dynamic bandwidth allocation, and dispersion tolerance, these technologies constitute potential substitutes for imparting local loop broadband services. This paper presents a thorough review and comparative analysis of the contemporary evolution in optical access network technologies with an accentuate on their functionality, mechanism, manoeuvre, and applications. We are also looking at the upcoming development of these innovations, including the incorporation of 100 G-EPON connectivity to meet the enhanced bandwidth demands precipitated by IP video, mobile broadband, and the Internet of things (IoT).

Dynamic bandwidth allocation algorithm based on extreme learning machine in passive optical networks

Dynamic bandwidth allocation algorithm based on extreme learning machine in passive optical networks

A predictive TDM-PON resource allocation using the ANN method based on equilibrium points of discrete dynamical systems

Resource allocation is one of the challenges in the time division multiple access (TDM) passive optical network (PON) area. Much research has been done to find an efficient resource allocation method. Using machine learning (ML) and finding proper ML algorithms in predictive dynamic bandwidth allocation remains an open research question. This paper aims to predict resource allocation accurately and efficiently based on historical requests. Artificial neural networks (ANN) with historically allocated bandwidth from different time series are used to build the prediction model. This model is constructed by the normal equation method. The proposed model needs a small dataset for its training phase, while its accuracy is higher than other models. The discrete dynamical system builds to find an equilibrium point, which is a stable point in the system. The proposed model can be implemented as a pre-training, or it can be dynamically refined when the model is running by using some of its output as feedback. The system evaluation verifies that the proposed DBA (EQ-ANN) elevates the quality of service (QoS) metrics, such as system throughput, packet drop probability, and packet means delay. The results show that the system throughput improves by up to 5%, and packet loss improves by up to 15%. Moreover, the prediction accuracy of the system reached 99.48%.

Auction-based hybrid DBA algorithm in EPON-WiMAX converge network for 5G and beyond (5 GB) communications

Managing large data volumes requires high-capacity networks due to increasing internet users and bandwidth-intensive applications. Optical fiber, particularly Ethernet passive optical network (EPON), is vital for cost-efficient last-mile connectivity. EPON handles data traffic, while WiMAX demands extensive bandwidth due to rising mobile users. Both the networks experience bursty user-generated traffic with fluctuating bandwidth needs. That’s why, the bandwidth requirements of each subscriber fluctuates dynamically. Moreover, access networks must provide distinct service quality to support a large number of users and ensure the requirement of 5G and beyond (5 GB) communication standards. To address these difficulties, in this paper, we propose a prediction-based hybrid dynamic bandwidth allocation (PHDBA) algorithm for EPON-WiMAX converge network. Due to the increasing value of trustworthy network service access, we have implemented a statistical modeling technique. Our technique predicts the heavily loaded ONUs from a randomly generated dataset and provides an approximation of the amount of data that can be strongly loaded. To make this prediction, we have proposed a statistical modeling technique and updated the auction-based semi dynamic bandwidth allocation (SDBA) algorithm. To validate our proposed algorithm, computer simulation is used to evaluate different performance parameters in terms of end-to-end delay, jitter, throughput, and fairness. From the comparative analyses, it is proved that our proposed scheme surpasses the competition over the existing SDBA algorithm.