Dynamic Bandwidth Allocation Method Research Articles

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.

Joint Power and Bandwidth Allocation with RCS Fluctuation Characteristic for Space Target Tracking

Reasonable allocation of space-based radar resources is a crucial aspect of improving the accuracy of space multi-target tracking and enhancing spatial awareness. The conventional resource allocation algorithm fails to exploit the high dynamic radar cross-section (RCS) characteristics, resulting in poor tracking robustness, tracking divergence, or even loss of tracking. However, the RCS of space targets fluctuates considerably in actual tracking scenarios, which cannot be disregarded for space target tracking tasks. To address this issue, we propose an adaptive allocation method that considers the dynamic RCS fluctuation characteristic for space-based radar tracking assignments. The proposed method exploits the predictable orbital information of space target to calculate the real-time observation angle of radar, and then obtains the multi-target dynamic RCS through the target RCS dataset. By combining the obtained RCS sequence, radar power, and bandwidth, an optimal model for radar tracking accuracy is established based on the multi-target posterior Cramér–Rao lower bound (PCRLB) to evaluate the tracking performance. By resolving the aforementioned multivariance optimization problem, we eventually acquire the results of power and bandwidth pre-allocation for tracking multiple space targets. Simulation results validate that, compared with the traditional methods, the proposed joint dynamic RCS power and bandwidth allocation (JRPBA) method can achieve superior tracking accuracy and minimize instances of missed tracking.

Research on prediction weighted interpolated polling dynamic bandwidth allocation for optical fiber bus based on gigabit passive optical network

Optical fiber bus technology is an important research direction in communication systems of electronic devices in special vehicle platforms. According to the requirements for communication quality between devices in special vehicle platforms, an optical fiber bus based on gigabit passive optical network topology is proposed. A data cache-based predictive weighted interpolated polling dynamic bandwidth allocation method is presented and investigated for the optical fiber bus’s upstream bandwidth allocation. The network controller establishes a weighted buffer area for the communication requirements in the polling period according to the type and data volume of the network terminal (NT) data stream and dynamically allocates the bandwidth of each optical NT to better meet the communication requirements for different data within the special device platform. Verification is done by means of OPNET software simulation and the establishment of a NT simulation test system in the laboratory. This proves that the proposed dynamic bandwidth allocation algorithm can reduce the end-to-end delay of RS422 port data by more than 50% and meet the Ethernet and controller area network port data delay requirements.

An Intelligent Dynamic Bandwidth Allocation Method to Support Quality of Service in Internet of Things

Worldwide, Internet of Things (IoT) devices will surpass a range of five billion by 2025 and developed countries will extend to advance by supplying almost two-thirds of such connections. With existing infrastructure, allocating bandwidth to billions of IoT devices is going to be cumbersome. This paper addresses the problem of Dynamic bandwidth allocation in IoT devices. We enhanced the dynamic bandwidth allocation algorithms to support QoS in different bandwidth ranges. Our Proposed innovative Machine learning-based Intelligent Dynamic Bandwidth Allocation (IDBA) algorithm allocates the bandwidth effectively between IoT devices based on utilization patterns observed through machine learning methods. Moreover, we showed that an IDBA algorithm results in supporting quality of service in terms of ensuring uninterrupted bandwidth to critical IoT application where bandwidth tolerance is zero percent, along with that IDBA increasing the network throughput correlated to other dynamic bandwidth allocation algorithms. We demonstrate simulations in different applications. The results show that IDBA achieves better throughput even in low bandwidth range.

Dynamic bandwidth allocation algorithm with demand forecasting mechanism for bandwidth allocations in 10-gigabit-capable passive optical network

10G-passive optical network (XG-PON) provides an emerging potential technology to carry multiple services for the first - mile access network. However, it offers 0.3 ms propagation delay between the optical network Unit (ONU) and optical line terminal (OLT) resulting in inevitable increased queuing delay for the bandwidth grants to the ONUs. We present a reasonable and effective uplink dynamic bandwidth allocation method - demand forecasting DBA (DF-DBA) that predicts ONU’s future demands by statistical modelling of the demand patterns and tends to fulfil the predicted demands just in time, which results in reduced delay. Results show that use of simple normal distribution in the DBA engine offers more appropriate bandwidth allocation with 14% decreased packet drop ratio (PDR) values than standard GigaPON access network (GIANT) DBA following request-grant cycle procedure. Proposed DBA when tested for all the T-CONTs, reduced 50% delay and offered 45% throughput improvement at higher traffic load. The unassigned bandwidth margin (UBM) is nearly ideal along with better QoS parameters results which validates that DF-DBA can practically improve the OLT performance. Further, this predictive DBA opens up a new horizon of research for researcher to use it for longer distances in PON (LR-PON) network with different statistical distributions for getting the possible best results in the future.

Traffic estimation based on long short-term memory neural network for mobile front-haul with XG-PON

A novel predictive dynamic bandwidth allocation (DBA) method based on the long short-term memory (LSTM) neural network is proposed for a 10-gigabit-capable passive optical network in mobile front-haul (MFH) links. By predicting the number of packets that arrive at the optical network unit buffer based on LSTM, the round-trip time delay in traditional DBAs can be eliminated to meet the strict latency requirement for MFH links. Our study shows that the LSTM neural network has better performance than feed-forward neural networks. Based on extensive simulations, the proposed scheme is found to be able to achieve the latency requirement for MFH and outperforms the traditional DBAs in terms of delay, jitter, and packet loss ratio.

Low Latency Dynamic Bandwidth Allocation Method with High Bandwidth Efficiency for TDM-PON

Low Latency Dynamic Bandwidth Allocation Method with High Bandwidth Efficiency for TDM-PON

Implementation of ultra-low latency dynamic bandwidth allocation method for TDM-PON

Implementation of ultra-low latency dynamic bandwidth allocation method for TDM-PON

Performance Evaluation of Dynamic Bandwidth Allocation Algorithm for XG-PON Using Traffic Monitoring and Intra-Scheduling

An XG-PON (10-Gbps-capable passive optical network) system is comprised of an OLT (optical line termination) and multiple ONUs (optical network units). In general, ONUs report their requests to the OLT and then the OLT performs a dynamic bandwidth allocation operation to allocate non-overlapping transmission slots to ONUs. In this paper, we consider an XG-PON system that ONUs do not report their requests to. The OLT estimates the ONU status by monitoring the upstream bandwidth usage of the ONU. We propose a dynamic bandwidth allocation method for the XG-PON system in which ONUs do not report their reports. The proposed method allocates a bandwidth to an ONU only if the ONU has fully used its upstream slot. Also, the proposed method periodically allocates a probe bandwidth to an ONU to prevent service starvation. Using simulation, we evaluate the performance of the proposed method under balanced and unbalanced traffic.

Prospects and challenges of context-aware multimedia content delivery in cooperative satellite and terrestrial networks

Cooperative satellite cells and terrestrial wireless communication networks (comprising macrocells, picocells, and the Internet) are much anticipated access technologies to enable users to seamlessly access rich multimedia content (e.g., TV broadcasting, VoD streaming, and other services) on their devices. Furthermore, context awareness has become popular to accompany these services not only to enhance the users' perceived service quality but also to improve the overall utilization of such cooperative networks. However, delivering context-aware multimedia content through these cooperative networks is associated with a significant research challenge due to the inherently different satellites and terrestrial networks technologies. For example, to provide context-specific TV programs to a user, a satellite needs to adopt unicast-style delivery. This may be an expensive operation since satellites are intrinsically useful for broadcasting or multicasting services to a wide audience. In contrast, terrestrial communication networks can be better suited to perform unicast-based context-aware content delivery. In this article, we address these challenges, and propose a dynamic bandwidth allocation method to effectively utilize the satellite and terrestrial networks for providing context-aware contents to many users. Computer-based simulation results are presented to demonstrate the effectiveness of our proposed method.

Handover rate based dynamic guard bandwidth allocation method for call admission control in IEEE 802.16j Mobile Multihop Relay networks

IEEE 802.16j MMR (Mobile Multihop Relay) networks are developed to extend the coverage area and enhance the throughput of IEEE 802.16 networks. Call Admission Control (CAC) is an important research issue related to these networks. In this paper, a novel dynamic guard bandwidth allocation method is proposed for CAC in IEEE 802.16j networks; the guard bandwidth allocated at a station depends upon the handover rates from its neighbors and at its descendent stations. A bandwidth wastage prevention policy is proposed to utilize the unused guard bandwidth to fulfill the requests of lower priority calls and eventually to improve the bandwidth utilization of the network. Based on the total amounts of the guard bandwidth received by the lower priority calls, a novel bandwidth borrowing scheme is proposed. A Markov model is developed to analyze the performance of the proposed CAC method; analytical handover call dropping probabilities and new call blocking probabilities of various service classes are obtained using the proposed Markov model. Simulation experiments are conducted to compare the performance of the proposed CAC method with a fixed guard bandwidth based CAC method. The proposed method showed superior performance than the fixed guard bandwidth based method. Also, the simulation results are compared with the analytical results.

TWDM PON을 위한 새로운 MAC 프로토콜 및 동적대역할당 방법

PONs (passive optical networks)은 운용비용이 저렴하고 높은 대역폭을 제공하기 때문에 가입자망에서 각광을 받고 있다. 최근에는 WDM (wavelength-division multiplexing)와 TDM (time-division multiplexing)을 결합한 TWDM PON이 가입자망에서의 새로운 해답으로 여겨지고 있다. 본 논문에서는 TWDM PON에 대한 새로운 MAC (media access control) protocol을 제안한다. 또한 TWDM PON에 대한 새로운 동적대역할당 방법을 제안하고 성능을 평가한다. Passive optical networks (PONs) are the emerging solution for access networks since PONs provide high bandwidth and the low operation cost. In recent, the new solution in access networks is TWDM PON which is a mixture of WDM (wavelength-division multiplexing) and TDM (time-division multiplexing). This paper proposes a new MAC (media access control) protocol for TWDM PON. In addition, this paper proposes and evaluates a new dynamic bandwidth allocation method for TWDM PON.

Low-Latency Dynamic Bandwidth Allocation for 100 km Long-Reach EPONs

We propose a new dynamic bandwidth allocation (DBA) method for long-reach passive optical networks that can reduce the upstream latency. With this method, an optical line terminal allocates bandwidth to long-distance (up to 100 km) optical network units (ONUs) with a transmission request prediction and reduces the latency of the long-distance ONUs. This has no influence on the latency of short-distance ONUs even if they coexist with long-distance ONUs. The results of real machine experiments show that this new DBA method achieves a smaller latency with long-distance ONUs than the conventional DBA technique, and that there is no influence on short-distance ONUs. The latency time and jitter are kept below 1300 μs and 1000 μs, respectively, for both long- and short-distance ONUs. The experimental results also reveal the effect of the proposed method on bandwidth utilization efficiency and fairness. The degradation in bandwidth utilization efficiency when one long-distance ONU is added is small (0.3%), and the fairness index degradation is negligible (less than 0.1%), when the parameters are optimized. These results indicate the validity of the proposed DBA method.

Arishtat: auction-based dynamic bandwidth allocation method in Ethernet passive optical networks

The increasing applications of networking have caused limitation of dynamic bandwidth allocation (DBA) in next generation access networks such as Ethernet passive optical network (EPON). In this paper, Arishtat, a novel EPON-based DBA method has been proposed that shows advantages over currently available methods. In Arishtat, which is based on the auction theory, optical line terminal runs an auction-based process to effectively register optical network units bandwidth requests and allocates the highest bidders based on the amount of available bandwidth. Simulation results indicate significant improvements comparing to Fair sharing using dual-service-level agreements, and ‘limited service’ interleaved polling with adaptive cycle time methods regarding quality of service parameters such as packet loss, throughput, line utilisation and the execution time of running method.

Burst-Polling-Based Dynamic Bandwidth Allocation Using Adaptive Minimum Guaranteed Bandwidth for EPONs

The enhanced burst-polling dynamic bandwidth allocation (EBDBA) method is proposed to support broadband access networks based on quality of service for Ethernet passive optical networks (EPONs). EBDBA adaptively increases or decreases the minimum guaranteed bandwidth of the three traffic clasess?expedited forwarding (EF), assured forwarding (AF), and best effort (BE) traffic?according to the requested bandwidth of an optical network unit. Therefore, network resources are efficiently utilized and adaptively allocated to the three traffic classes for unbalanced traffic conditions. Simulation results using OPNET show that EBDBA outperforms conventional bandwidth allocation schemes in terms of the average packet delay (it decreases the maximum performance range to 68%) and the network throughput (it increases the maximum performance range to 20%) at a given offered load of 1.2.

An auction-based dynamic bandwidth allocation with sensitivity in a wireless networked control system

Applications of control systems on wireless networks have been widely utilized due to their mobility. However, the performances of these networked control systems (NCS) could be degraded and become unstable by network-induced delays. Existing dynamic bandwidth allocation methods for NCS assign bandwidth to each system with respect to different priorities in an ascending order. However, these NCS may not be given bandwidths at equilibrium such that each of these NCS is satisfied with respect to bandwidth requests of other NCS. Therefore, some NCS may always consume most of given bandwidths, while others may never be given satisfied bandwidths. This paper proposes a dynamic bandwidth allocation methodology that controls bandwidths given to open-loop NCS to be at Nash equilibrium. In this paper, the average sensitivities of NCS are used in utility functions in order to evaluate the effects of network-induced delays for NCS. Then, a control center or an access point will use the proposed methodology to allocate bandwidths for all NCS based on Nash equilibrium. Simulations and experiments were setup from a set of DC motors controlled over a wireless network. Simulation and experimental results show good performances of the proposed methodology compared with three other methods.

Dynamic bandwidth allocation in GEO satellite networks: A predictive control approach

The paper presents a novel approach for dynamic bandwidth allocation (DBA) in GEO satellite networks based on adaptive predictive control. In the proposed DBA scheme each satellite gateway (SG) uses an adaptive predictor to forecast the future input traffic flow along with a bandwidth controller to generate a bandwidth request to the bandwidth manager (BM) according to a receding-horizon policy, via solution of an optimal control problem. A multi-service traffic scenario is considered with a careful eye to guarantee differentiated quality of service requirements. Simulation experiments demonstrate that the predictive DBA technique proposed in this work outperforms fixed allocation as well as existing reactive DBA methods.

Ethernet PONs: a survey of dynamic bandwidth allocation (DBA) algorithms

Optical networks are poised to dominate the access network space in coming years. Ethernet passive optical networks, which leverage the ubiquity of Ethernet at subscriber locations, seem destined for success in the optical access network. In this article we first provide a brief introduction to Ethernet passive optical networks, followed by a discussion of the problem of dynamic bandwidth allocation. We then introduce a framework for classifying dynamic bandwidth allocation schemes and provide a comprehensive survey of the dynamic bandwidth allocation methods proposed to date. We conclude with a side by side comparison of the schemes based on their most prominent characteristics, and outline future developments of dynamic bandwidth allocation schemes.