Dynamic Bandwidth Allocation Scheme 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.

Quality of Service Aware Dynamic Bandwidth Allocation for Rate Control in WSN

Different types of data can be generated by Wireless Sensor Networks (WSNs) in both Real-Time (RT) and Non-RT (NRT) scenarios. The combination of these factors, along with the limited bandwidth available, necessitates careful management of these categories in order to reduce congestion. Due to this, a Proficient Rate Control and Fair Bandwidth Allocation (PRC-FBA) method has been created that prioritizes certain types of traffic and creates a virtual queue for them.In PRC-FBA, the Signal-to-Noise and Interference Ratio (SINR) model is applied to the problem of bandwidth allocation in WSN in an effort to find a compromise between equity and performance. Then, a brand-new bandwidth utility factor is defined with regard to equity and effectivenes. The FBA method in PRC-FBA is devoped for only improving throughput, but not considering delay. However, delay is the main factors for trasnmiitng NRT packets. This paper offers a PRC with Quality of Service (QoS) aware Dynamic Bandwidth Allocation (PRC-QDBA) approach for allocating bandwidth while prioritizing packets based on their traffic classes. This model employs a QoS associated dynamic bandwidth allocation strategy which efficiently distributes the unused time slots among the required nodes. The distribution technique is performed based on hierarchical manner utilizing a parent-child association of tree topology. The parent node receives traffic indication maps (TIMs) from the children nodes and adopts them to allocate time slots based on their demamds. If the parent node is unable to allocate the required slots, it creates a TIM that indicating the demands and transfer it to its immediate parent node. This increases the entire performance rate of RT traffic. Furthermore, this model assures the packet forwarding for previously accepted flows by allowing node transmission based on ancestral connection capabilities. Finally, simulation results demonstartes that the suggested model significantly increases the throughput and delay for bandwidth allocation while also enabling QoS support for RT traffic in WSNs.

A Distance-Weighted Dynamic Bandwidth Allocation Algorithm for Improved Performance in Long-Reach Passive Optical Networks for Next Generation Networks

In recent years, there has been an increasing trend towards extending the coverage of passive optical networks (PONs) over large geographical areas. Long-reach PONs (LRPONs) are capable of extending the distance covered by PONs from 20 km to 100 km, leading to cost savings in the network operation by reducing the number of central offices. They have become widely deployed due to their ability to provide high-speed, long-distance data transmission over optical fibers. In addition, the next generation of optical access networks are expected to provide high-capacity mobile and wireless backhauling over a wide coverage area. However, this extended reach also requires the design of efficient dynamic bandwidth allocation (DBA) schemes to address the performance degradation caused by the increased propagation delay in LRPONs. The DBA schemes commonly used for upstream traffic transmission in traditional PONs are not well-suited for use in LRPONs due to their inefficiency in bandwidth utilization due to the increased round-trip time (RTT) between the optical line terminal (OLT) and the optical network unit (ONU). In this study, we present an efficient DBA algorithm, the Distance-Weighted Bandwidth Allocation DWDBA Algorithm, specifically enhanced for multi-wavelength LRPONs. Our DBA algorithm utilizes a scheduling policy that assigns weight vectors to Optical Network Units (ONUs) based on their distance from the Optical Line Terminal (OLT), sorting them accordingly without penalizing any ONU due to their distance. The DWDBA takes the laser tuning time into consideration. We conducted extensive simulations to evaluate the performance of the proposed algorithm under various scenarios and compared it to the IPACT algorithm. The results of the simulations show that the proposed algorithm outperformed the IPACT algorithm in terms of bandwidth utilization and queue delay.

Towards 6G: fast and self-adaptive dynamic bandwidth allocation for next-generation mobile fronthaul [Invited

6G networks will deliver dynamic and immersive applications that bridge the real and digital worlds. The next-generation passive optical access network is a potential optical transport solution for the fronthaul of open radio access networks. With this solution, uplink bandwidth is shared, and uplink latency performance is thus highly dependent on how bandwidth is allocated. Compounding this issue is that future mobile fronthaul (MFH) is expected to support a range of applications that could vary in terms of traffic patterns, bit rates, etc. In view of dynamic network conditions, we present in this paper a machine learning driven dynamic bandwidth allocation scheme that rapidly learns to optimize bandwidth allocation decisions to satisfy uplink latency requirements. The principle of operation of the scheme detailing the reinforcement and transfer learning framework is first described. Performance evaluation results implemented on a target empirical network are then presented. Results show that self-adaptive bandwidth decisions can be rapidly achieved in response to different traffic patterns, network loads, and line rates, consolidating the potential of the dynamic allocation scheme in supporting diverse applications in future MFH networks.

Improving the Timeliness of Two-Source Status Update Systems in Internet of Vehicles With Source-Dedicated Buffer: Resource Allocation

Age of information (AoI) is a pertinent metric for characterizing the timeliness of status updates in Internet of Vehicles (IoV). Resources allocated to update transmission would significantly affect the timeliness of status update observed at the receiver end. This work investigates the sum average AoI of a two-source wireless updating system in IoV by modeling the system as a non-preemptive queue with a size-two source-dedicated buffer. Two bandwidth allocation schemes, i.e., an asynchronous time division multiplexing (ATDM) scheme and a dynamic bandwidth allocation (DBA) scheme, are proposed for the system. In the ATDM scheme, status updates are transmitted using all available bandwidth sequentially while in the DBA scheme, the server fractionally allocates the available bandwidth for transmitting the arrived update based on the buffer state. By analyzing the age evolution of each update stream based on stochastic hybrid systems, the closed-form per-source average AoI and the sum average AoI of the two proposed schemes are derived. In particular, the per-source average AoI when the system provides priority for serving one update stream is also given. It is demonstrated that by employing the ATDM scheme and the DBA scheme with optimized bandwidth allocation factor, the system can achieve a significant improvement in terms of sum average AoI, enhancing the update freshness.

Machine learning enhanced next-generation optical access networks—challenges and emerging solutions [Invited Tutorial

Optical access networks are envisioned to become increasingly complex as they support more and more diverse and immersive services, each with a different capacity, latency, and reliability need. While machine learning has been touted as a silver bullet that will intelligently manage network operations and resources to meet these demands, as it had been anticipated for core and metro networks, there exist various challenges that need to be addressed to progress machine learning models from research to production. In this tutorial, we first aim to motivate the continued push to advance optical access networks and rationalize the use of machine learning in these networks. We then highlight the challenges that are especially amplified due to the traffic dynamicity and heterogeneity, data scarcity, and computation-resource constraints of optical access networks. We discuss emerging machine learning approaches that are being explored to address these challenges. Finally, we consider a fast and self-adaptive machine learning enhanced dynamic bandwidth allocation scheme in an illustrative future use case of supporting immersive human-to-machine communications over the mobile fronthaul of next-generation mobile networks.

Multi-Task Hybrid Scheduling Scheme in FC-AE-1553 Multi-Level Switching Networks

Given the increasing need of avionics communications and ship electromechanical communications for reliability and real-time transmission, current scheduling schemes waste bandwidth resources. They increase delay because of the low concurrency and the inefficiency of data transmissions in FC-AE-1553 multi-level switching networks. In this study, we first analyze the characteristics of different tasks in the field of avionics communication and establish the FC-AE-1553 network model. We then propose a multi-task hybrid scheduling scheme that includes a static scheduling scheme based on the flow and common divisors of the periods to improve the utilization of bandwidth while ensuring the periodic transmission of deterministic tasks. Further, we exhibit a dynamic bandwidth allocation scheme based on flow and credit ranking to increase throughput and fairness while decreasing the delay of burst tasks. Specifically, to ensure the periodic transmission of deterministic tasks, we divide the transmission time slots according to the common divisor of the period and schedule them in ascending order of period and flow. We update the weights of the credit ranking and flow online, and schedule them in descending order of weight size. The experimental results show that our schemes improve the bandwidth utilization of deterministic tasks by 10% and can balance the throughput and delay of the network. By improving the bandwidth utilization of deterministic tasks, we can provide a better quality of service for burst tasks. When the network can balance its throughput and delay, this shows that the network is efficient and stable.

Enhancing coordinated multi-point joint reception with dynamic bandwidth allocation

Coordinated Multipoint (CoMP) Joint Reception (JR) is a vital technology to mitigate uplink inter-cell interference (ICI) and can be facilitated by the centralized radio access network (C-RAN). In C-RAN architecture, base stations (BSs) are decoupled into the central unit (CU) and the distributed units (DUs). Meanwhile, a fronthaul (FH) is employed to connect CU and DUs. However, FH constructed by the time division multiplexing passive optical network (TDM-PON) significantly affects the performance of JR. The inherent polling mechanism of TDM-PON results in the asynchronous forwarding of JR data, which leads to high JR latency and a large amount of storage consumption. To alleviate the problem, we presented a novel dynamic bandwidth allocation (DBA) scheme, which classifies network data as JR and non-JR data and optimizes the data forwarding order of different types and DUs. A heuristic algorithm called Rearrange Sub-timeslot DBA (RS-DBA) was proposed to implement the DBA scheme. Simulation results indicate the proposed RS-DBA algorithm can reduce the average latency of JR data and storage resources consumption. In addition, the RS-DBA can make a better tradeoff between latency and efficiency.

A hybrid dynamic bandwidth allocation scheme operating with IACG and cooperative DBA for converged fronthaul networks

The time-division multiplexed passive optical network is seen as a candidate for converged fronthaul networks in 5G, which requires low latency and high capacity. In this paper, a hybrid dynamic bandwidth allocation (DBA) scheme for converged fronthaul that combines a status reporting DBA and a cooperative DBA is proposed. Different deployment scenarios and operating conditions in a 10-Gigabit-capable PON (XGS-PON) system are investigated using the OMNeT++ network simulator. The performance of the proposed hybrid DBA is evaluated in terms of average upstream delay and percentage of frame loss meeting a 140 µs queuing delay requirement for fronthaul traffic. Simulation results show a zero-frame loss for fronthaul traffic in all scenarios using the proposed hybrid DBA which is not achievable using only the status reporting DBA, confirming the advantages of the proposed hybrid DBA.

A novel dynamic bandwidth allocation scheme towards improving the performance of XG-PON system

Time division multiplexed passive optical networks (TDM-PONs) such as EPON, XG-PON, XGS-PON, etc. have become widely accepted network architectures for meeting the bandwidth requirement of high-end consumer applications. The available bandwidth is still limited and needs to be utilized efficiently. There are scenarios when one ONU may completely dominate the network bandwidth leading to starvation of another ONUs. This may happen for an ONU in every allocation cycle. This enforces the OLT to provide such a fair allocation scheme that would provide a chance to different ONUs during polling and scheduling mechanism. This paper presents two novel DBA schemes namely the analytic hierarchy process proportional allocation scheme (AHP-PAS) and weighted sum model proportional allocation scheme (WSM-PAS) to determine the order of a transmission container (T-CONT) of an ONU for polling and scheduling. The proposed schemes are implemented in the network simulator (NS-3) and compared with the existing proportional allocation scheme (PAS) for inter-ONU and intra-ONU. The obtained results confirm that the proposed DBA schemes significantly improve the network performance in terms of delay fairness index, upstream mean delay, packet loss ratio, and packet jitter for T-CONT 2, 3, and 4.

Proposed an efficient multilevel dynamic bandwidth allocation (M-DBA) scheme for FiWi networks

Proposed an efficient multilevel dynamic bandwidth allocation (M-DBA) scheme for FiWi networks

Bayesian auction game theory-based DBA for XG symmetrical PON.

The next-generation passive optical networks (NG-PONs) (i.e., 50G-PON and Time-division-multiplexing/Wavelength-division-multiplexing, TWDM-PON) offer very high bandwidth with improved quality of service. In these PONs, the role of efficient Dynamic bandwidth allocation (DBA) becomes even more important in reducing the upstream delays, bandwidth waste and reducing the upstream delays and delay variance. These qualities of service metrics lead to improved Quality of Experience (QoE) for the end-users in addition to increased revenue for the service providers. This study introduces the game theory concept in the bandwidth distribution process in PON. Specifically, the Bayesian auction game theory (BAGT) process is used in the DBA process to address the unfair and inefficient distribution of upstream bandwidth to the optical network units (ONUs) in XG symmetrical PON(XGs-PON). The proposed BAGT scheme allocates the excess bandwidth to the entire ONUs in proportion to their demands reported via the bidding process. To validate the performance of the BAGT scheme, we also compare it with other existing DBA schemes namely; proportional allocation schemes (PAS), improved bandwidth utilization (IBU), and optimized round-robin (ORR) methods. The simulation results show that the proposed scheme results in higher system throughput and lower upstream delays than the other schemes. BAGT DBA also improves the bandwidth utilization by up to 38% to 50% compared to IBU, ORR, and PAS schemes and exhibits the minimum frame loss ratio.

Dynamic Beam Pattern and Bandwidth Allocation Based on Multi-Agent Deep Reinforcement Learning for Beam Hopping Satellite Systems

Due to the non-uniform geographic distribution and time-varying characteristics of the ground traffic request, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for beam hopping satellite systems. The conventional greedy-based beam hopping methods do not consider the long-term reward, which is difficult to deal with the time-varying traffic demand. Meanwhile, the heuristic algorithms such as genetic algorithm have a slow convergence time, which can not achieve real-time scheduling. Furthermore, existing methods based on deep reinforcement learning (DRL) only make decisions on beam patterns, lack of the freedom of bandwidth. This paper proposes a dynamic beam pattern and bandwidth allocation scheme based on DRL, which flexibly uses three degrees of freedom of time, space and frequency. Considering that the joint allocation of bandwidth and beam pattern will lead to an explosion of action space, a cooperative multi-agents deep reinforcement learning (MADRL) framework is presented in this paper, where each agent is only responsible for the illumination allocation or bandwidth allocation of one beam. The agents can learn to collaborate by sharing the same reward to achieve the common goal, which refers to maximize the throughput and minimize the delay fairness between cells. Simulation results demonstrate that the offline trained MADRL model can achieve real-time beam pattern and bandwidth allocation to match the non-uniform and time-varying traffic request. Furthermore, when the traffic demand increases, our model has a good generalization ability.

Enhancing Energy Efficiency of the Doze Mode Mechanism in Ethernet Passive Optical Networks Using Support Vector Regression

An Ethernet passive optical network (EPON) is an optical access network that allows a higher data rate with low power consumption. To improve energy savings for an EPON, the sleep and doze modes for the optical network units (ONUs) play a pivotal role. Many prediction schemes have been proposed to control these modes. To increase the prediction accuracy, this study proposes an energy-efficient approach that uses a support vector regression (SVR) model. A dynamic bandwidth allocation (DBA) scheme called SVR-DBA is designed to allocate bandwidth to ONUs more efficiently and fairly. To determine the effectiveness of the proposed scheme, simulations are performed. The simulation results show that the proposed scheme decreases energy consumption for ONUs by up to 47% and fulfills the quality-of-service (QoS) requirements in terms of delay, jitter, and packet loss.

A Novel Dynamic Bandwidth Allocation Scheme for XGPON based Mobile Fronthaul for Small Cell CRAN

Time division multiplexing passive optical networks (TDM-PON) have emerged as a promising technology for mobile fronthaul for small cell CRAN. The performance of such TDM-based PON mobile fronthaul for small cell CRAN greatly depends on efficient dynamic bandwidth allocation (DBA) mechanisms. In this paper, a novel DBA mechanism based on the concept of dynamic service interval for ITU-based TDM-PON (XG-PON) for mobile fronthaul has been proposed. The proposed scheme is implemented using network simulator −3 (NS-3) simulation tool for evaluation of network performance parameters. The performance evaluation of the proposed DBA against the existing schemes such as optimized round robin (ORR) and Group-GIANT DBA shows a significant improvement in upstream mean delay, packet loss ratio and grant-request ratio. In addition to this, the proposed scheme shows that larger number of ONU(RRH) can be supported by the network for front haul uplink traffic transmitted via XG-PON in small cell CRAN.

Generalized Multi-Access Dynamic Bandwidth Allocation Scheme for Future Generation PONs: A Solution for Beyond 5G Delay/Jitter Sensitive Systems

In this paper, we present a novel dynamic bandwidth allocation (DBA) scheme for future generation passive optical networks (PON) in order to meet the beyond 5G (B5G) and sixth generation (6G) stringent requirements. Our proposed algorithm is a generalized multi-access DBA (GMA-DBA) that exploits both orthogonal and non-orthogonal multi-access (MA) resources, resulting in a radical enhancement in the network performance. The proposed GMA-DBA algorithm is modeled as an optimization problem for which a closed form solution is derived. Results show that the GMA-DBA is capable of accommodating a network throughput higher than 1 Tbps while securing a latency and jitter below 100 µs and 10 µs, respectively. It can also maintain reliability, simplicity and energy efficiency. Therefore, using a non-orthogonal MA layer seems to be inevitable to achieve the stringent performance needed for the B5G and 6G next generation systems.

Traffic localization-based dynamic bandwidth allocation algorithm for improvement of QoS in peer-to-peer architecture of passive optical networks

Passive optical network (PON) is a popular technology, in recent days, which can be merged with peer-to-peer (P2P) network architecture. In P2P architecture, there is no need for a dedicated server and each peer can provide the data and receive the required data from other peers. With increasing traffic, there occurs aproblem in resource sharing and therefore, the users experience higher latency and lower overall quality of services (QoSs). There are several recent studies regarding the use of PON in P2P network architecture, and some of them are studied for localization of traffic. But in the case of localizing traffic, the use of multiple upstream wavelengths was not studied previously. In this paper, we have proposed a new PON architecture and a new dynamic bandwidth allocation (DBA) algorithm called localizing traffic-based DBA (LT-DBA) to improve the overall QoSs of the PON-based P2P network. By localizing the intra-PON traffic, this architecture separates the inter-PON and intra-PON traffic. Here, in addition to the conventional passive optical splitter, a wavelength reflector is used which can reflect the upstream wavelength used for intra-PON traffic. We also propose an efficient management scheme of the intra-PON traffic, where no report is needed to send to the optical line terminal (OLT). The OLT needs not to allocate time-slot for local packets, and there is a huge optimization of resources. The LT-DBA scheme was evaluated by formulating mathematical models and numerical simulations. The simulations were performed by varying the percentage of local data and by changing the number of optical network units in PON. The simulation results show that our proposed architecture and LT-DBA scheme outperform the existing REDIRECT DBA scheme in terms of end-to-end delay, jitter, and throughput.

Cluster-based PON with dynamic upstream data transmission sequence algorithm for improving QoSs

Passive optical network (PON) with online polling-based dynamic bandwidth allocation (DBA) algorithms effectively reduces the granting and back-off delays as well as the overall end-to-end delay of the PON with offline polling-based DBA schemes. However, in the online polling-based DBA schemes, distribution of excess bandwidths from the lightly loaded optical network units (ONUs) to the highly loaded ONUs is not possible. In both the conventional online and offline polling-based DBA schemes, theoptical line terminal (OLT) assigns the time-slots to the ONUs for upstream data transmissions based on the first-come-first-serve principle. That is why the highly loaded ONUs those remainbehind the lightly loaded ONUs should be waited until the end of the upstream data transmissions of all the preceding ONUs. Due to this longer waiting time before getting the chance to transmit the upstream data increases the possibility of buffer overflow of the highly loaded ONUs. In this paper, a new cluster-based PON architecture with a dynamic upstream data transmission sequence (DUDTS) algorithm is introduced. In the cluster-based PON with DUDTS scheme, all the active ONUs of a PON system are divided into several clusters. Then the data transmission sequence of the ONUs in each cluster is dynamically changed to give higher priority to the highly loaded ONUs for assigning the earlier time-slots than the lightly loaded ONUs. The DUDTS scheme also considers the traffic incoming statuses in the ONUs’ buffer,distributes the excess bandwidth from the lightly loaded ONUs to the highly loaded ONUs within a cluster, and adds the unutilized bandwidth of a cluster to the ONUs of thefollowing cluster. Numerical simulations are performed to evaluate and compare the performances among the proposed cluster-based PON with DUDTS scheme and the existing dynamic hybrid slot-size bandwidth allocation (DHSSBA) algorithm to demonstrate the effectiveness of the proposed scheme. The cluster-based PON with DUDTS scheme provides better results in terms of the grant to request ratio, end-to-end delay, packet loss probability, and fairness compared to the existing DHSSBA scheme.

Dynamic bandwidth allocation based on adaptive predictive for low latency communications in changing passive optical networks environment

The industry is considering redesigning the architecture of optical access networks (PONs) to address the challenges of 5G developments. One of the major challenges is to achieve low communication delay. To better address this challenge, PONs not only need to be improved from the perspective of underlying facilities, but also need to combine with effective resource allocation strategies. This paper proposes a dynamic bandwidth allocation strategy based on adaptive predictive algorithm. First, the amount of arriving packets at each ONU during each polling cycle is predicted using the XGBoost ensemble learning algorithm. As such, arriving packets can be allocated bandwidth in advance, so as to achieve low communication delay. Simulations verify that the DBA algorithm based on XGBoost is basically the same as the DBA algorithm based on neural network algorithm in terms of delay performance, but requires less computing time. At the same time, for increasing the robustness of the algorithm, dealing with the changing PONs environment (e.g., the number of ONUs online in PONs changes over time caused by sleep mode), we use dynamic perceptual algorithm which uses the ideas of reinforcement learning to adjust the result of the predictive algorithm, this perceptual algorithm helps to reduce the predictive error caused by environmental changes, so as to reduce the error impact on achieving low delay. Through simulation experiments with different PON environments, the effectiveness of the proposed algorithm is verified and the delay effect of the original predictive algorithm is improved by nearly 10% with the adaptive predictive algorithm.

Fair and efficient DWBA algorithm based on SLA differentiated polling interleaved scheduling for NG-EPON

This study, based on the Next Generation Ethernet Passive Optical Network (NG-EPON) facing more complex and changeable network, develops a flexible and efficient resource allocation algorithm. First, a differentiated polling interleaved scheduling (DPIS) framework is designed based on dynamic awareness, significantly reducing idle time slots, improving channel upstream utilization, and performing service-level-agreement (SLA) support and Optical Network Unit (ONU) overall scheduling. Subsequently, a dynamic wavelength and bandwidth allocation (DWBA) scheme is presented, which allocates fair bandwidth between and in subcycles and ensures the timely transmission of large traffic ONUs. As revealed from the simulation results, the developed DPIS-DWBA algorithm exhibits high utilization and low latency performance of the online scheduling algorithm; it can also achieve the offline scheduling to support SLA and fairness and offers more effective delay differentiation and bandwidth fair service.