Predictive Dynamic Bandwidth Allocation Research Articles

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%.

Machine Learning-Based Bandwidth Prediction for Low-Latency H2M Applications

Human-to-machine (H2M) communications in emerging tactile-haptic applications are characterized by stringent low-latency transmission. To achieve low-latency transmissions over existing optical and wireless access networks, this paper proposes a machine learning-based predictive dynamic bandwidth allocation (DBA) algorithm, termed MLP-DBA, to address the uplink bandwidth contention and latency bottleneck of such networks. The proposed algorithm utilizes an artificial neural network (ANN) at the central office (CO) to predict H2M packet bursts arriving at each optical network unit wireless access point (ONU-AP), thereby enabling the uplink bandwidth demand of each ONU-AP to be estimated. As such, arriving packet bursts at the ONU-APs can be allocated bandwidth for tranmission by the CO without having to wait to transmit in the following transmission cycles. Extensive simulations show that the ANN-based prediction of H2M packet bursts achieves >90% accuracy, significantly improving bandwidth demand estimation over existing prediction algorithms. MLP-DBA also makes adaptive bandwidth allocation decisions by classifying each ONU-AP according to its estimated bandwidth, with results showing reduced uplink latency and packet drop ratio as compared to conventional predictive DBA algorithms.

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.

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.

Predictive dynamic bandwidth allocation for efficient transport of real-time VBR video over ATM

This paper presents a novel approach to dynamic transmission bandwidth allocation for transport of real-time variable-bit-rate video in ATM networks. Video traffic statistics are measured in the frequency domain. The low-frequency signal captures the slow time-variation of consecutive scene changes while the high-frequency signal exhibits the feature of strong frame autocorrelation. Our queueing study indicates that the video transmission bandwidth in a finite-buffer system is essentially characterized by the low-frequency signal. We further observe in typical JPEG/MPEG video sequences that the time scale of video scene changes is in the range of a second or longer, which localizes the low-frequency video signal in a well-defined low-frequency band. Hence, in a network design it is feasible to implement dynamic allocation of video transmission bandwidth using on-line observation and prediction of scene changes. Two prediction schemes are examined: recursive least square method and time delay neural network method. A time delay neural network with low-complexity high-order architecture, called "pi-sigma network," is successfully used to predict scene changes. The overall dynamic bandwidth-allocation scheme presented is shown to be promising and practically feasible in obtaining efficient transmission of real-time video traffic.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>