Individual Traffic Flows Research Articles

Traffic Prediction as a Multidimensional Random Process in a Three-Dimensional High-Density Internet of Things Network

Relevance. The relevance of the topic considered in the article lies in the active transformation of communication networks and the formation of a three-dimensional high-density communication network, which changes the structure of data traffic, therefore, for this type of network, a traffic model is considered as a multidimensional random process. The main purpose of the study is to improve the efficiency of network traffic forecasting by developing a method, the distinctive feature of which is traffic forecasting as a multidimensional random process, taking into account the mutual dependence of individual flows produced by network nodes. Methods. The paper considers an algorithm for training an artificial neural network (ANN) based on the method of reducing the root of the mean square error RMSE, and also proposes forecasting methods using LSTM-type ANNs and adapting model parameters to changing network operating conditions. The use of LSTM-type ANN for forecasting a multivariate random process describing traffic in a three-dimensional high-density network can yield better results than forecasting individual traffic flows as independent random processes due to the consideration of mutual influences between different traffic flows.The results. Building the corresponding model, collecting statistics (obtaining a training sample), training the ANN and performing the forecast require the use of computing resources. Thus, the forecasting efficiency can be defined as a decrease in the forecasting error while maintaining the volume of resources used or a decrease in the volume of resources while maintaining the forecasting error. In the course of solving the scientific problem, criteria were identified for selecting the value of a unit interval (lag), which, together with the forecasting interval, significantly affects the final scenario.The theoretical significance The scientific novelty of the work lies in the assessment of the change in the error in forecasting the traffic of a three-dimensional high-density communication network as a multivariate random process, compared to presenting the forecast of the same traffic as a set of independent random processes.Significance (theoretical). The efficiency of traffic forecasting as a multidimensional random process in a three-dimensional high-density communication network increases with increasing dimensionality. Thus, such traffic in forecasting problems should be considered as a multidimensional random process, the dimensionality of which is equal to the number of network nodes producing traffic.Significance (practical). The results obtained in the work can be used in the future to optimize the functioning of the traffic management system.

Developing deep learning models for predicting urban bike-sharing usage patterns

Urban traffic systems are facing significant challenges due to the ever-growing number of vehicles on the road, leading to increased congestion and suboptimal traffic flow. Traditional research focusing on individual traffic flows is often insufficient to meet the complex demands of modern urban transportation. While studying integrated shared single-vehicle flows offers a potential solution to mitigate these issues, the unique characteristics of shared bikes present substantial obstacles to accurate traffic flow research. These obstacles include the high liquidity, sparsity, and variability of shared bikes, the vagueness of travel characteristics, the lack of correlation between travel groups, and the unpredictability of travel patterns. The study endeavors to confront the challenges above by proposing an innovative model that correlates multiuser interactions and elucidates behavioral dynamics. This model utilizes a deep clustering method to analyze the evolution of superlarge-scale shared bike systems in Beijing. It uncovers the complex mechanisms governing user behavior and employs a neural network algorithm to predict shared bike users’ travel patterns effectively. By focusing on the theoretical and algorithmic aspects of behavioral dynamics for large-scale shared single-vehicle flows, this study offers a unique contribution to the field, with significant implications for multi-traffic flow management and urban planning in scenarios with extensive multi-traffic flows.

Simulation Studies of Link Group in Elastic Optical Networks Used in Internet of Things Solutions

Abstract The elastic optical network (EON) solution is the most flexible in terms of allocation of resources for various traffic streams transported in core part (edge-cloud) of Internet of Things (IoT) networks. This paper presents the results of a study on the determination of the occupancy distribution and the loss probability for particular traffic flows offered in EONs supporting IoT systems. The purpose of the research was to determine the impact of: the number of links making up a group, the numbers of frequency slot units (FSUs) in individual EON link, the traffic intensity of individual traffic flows, the capacity of the EON link (number of FSUs) and the number of FSUs requested by different traffic streams. The developed simulation program of link group of EON allows the calculation of the values of the occupancy distribution and loss probability. The results obtained from the simulation are presented on the graphs in the form of points with confidence intervals.

From Modeling to Optimizing Sustainable Public Transport: A New Methodological Approach

This paper explores the potential for connected public-transport (PT) mobility as an alternative to motorized private transport (MPT) in medium-sized cities. Despite the high demand for MPT, it occupies a lot of space and contributes to conflicts and reduced livability. The more sustainable mobility solution of PT, however, is often considered slow, unreliable, and uncomfortable. To overcome these issues, the authors investigate the state-of-the-art research of connected PT mobility, including ways to quantify mobility behavior, micro- and macro-simulations of traffic flow, and the potential of not-yet-established modes of transport such as Mobility on Demand (MoD) for last-mile transportation. MoD could reduce the drawbacks of PT and provide sufficient and sustainable mobility to all citizens, including those in rural areas. To achieve this, precise information on individual traffic flows is needed, including origin–destination (OD) relations of all trips per day. The paper outlines a two-step approach involving the expansion of OD relations to include all modes of transport and diurnal variation, followed by microscopic traffic simulations and macroscopic optimization to determine potentials for on-demand offers within inner-city traffic. The paper concludes by calling for critical questioning of the approach to validate and verify its effectiveness.

A reputation-based framework to support dynamic car-pooling1,2

In the last decades, individual urban traffic flows have increased all over the world with a consequent growth of road congestion and environmental pollution. In this context, car-pooling is an interesting car-based alternative to satisfy the individual mobility demand by optimizing the car loading factor with respect to the number of passengers, provided that all the participants share trip origin and destination in the same time slot. To make the system more appealing, this paper proposes an on-demand car-pooling service adopting variable fares, on the basis of trip length and number of participants. Multi-agent, reputation and recommender system technologies in synergy with a routing algorithm have been used to this aim. Experiments on simulated data proved the potentiality of the proposed approach.

Network based definition of functional regions: A graph theory approach for spatial distribution of traffic flows

Functional regions are autonomous (internally coherent and externally self-contained) spatial structures based on vector data, so-called spatial interactions. Typically, travel-to-work, travel-to-school flows and migrations are analysed by various methods of functional regional taxonomy in order to define functional regions. There is still another type of statistically recorded vector data which has, up to now, rarely been used for this purpose: traffic flows. However, these data differ distinctly from the above mentioned flows. In this paper we pursue two objectives: (i) to define functional transport regions based on a graph theoretic analysis of individual traffic flows, and (ii) to add knowledge to the issue of the self-containment of functional transport regions. The specific nature of transport data compared to the above-mentioned spatial interactions requires a specific methodological approach, which is presented in the paper. The existing graph theoretic procedures do not seem suitable for the definition of functional transport regions due to data specifics. Therefore our analysis is based on a rough analogy to the minimum cut method – we identify minimum flows in a graph representing a transport network. The territory of the Czech Republic is used as the example. Two regional systems are defined (based on 2000 and 2016 data) and compared in time. The paper achieves two main findings. First, the proposed methodological approach allows us to define autonomous functional transport regions, and the means to calculate their self-containment is discussed. Second, functional transport regions in the Czech Republic show unexpected stability over time compared to functional regions based on such spatial interactions as commuting flows.

On Analysis of Traffic Flow Demultiplexing Effectiveness

It is known that the demultiplexing of the individual traffic flow into several independent transport subflows can increase the speed of it. This statement is true for a single flow but its truth for themassivecase, when demultiplexing technics are applied to all traffic flows in the network of a single Internet Service Provider (ISP), is not obvious. The question arises, what impact the massive demultiplexing of traffic flows will have on the whole ISP network bandwidth. In this paper, this question is considered for the static case, when each flow is demultiplexed statically, i.e. every flow before its launching is demultiplexed into the same number of subflows. We developed a mathematical model that was used to construct a simulation model in order to obtain more accurate estimates of the network performance with and without flow demultiplexing. Using simulation model, network properties are defined, under which the demultiplexing of traffic flows is justified. We proved the correctness of the obtained results by emulating a network load based on a protocol stack virtualization for the same input. We considered various routing policies that can be used for massive demultiplexing. Special attention is paid to algorithms that allow you to build routes with minimal intersections, since using the nonintersecting routes with non-optimal cost can increase the network performance. The routes constructed with these algorithms were used both for the network performance analysis with demultiplexed flows and in the case of balancing non-demultiplexed flows.

Efficient Extraction of Network Event Types from NetFlows

To perform sophisticated traffic analysis, such as intrusion detection, network monitoring tools firstly need to extract higher-level information from lower-level data by reconstructing events and activities from as primitive information as individual network packets or traffic flows. Aggregating communication data into meaningful entities is an open problem and existing, typically clustering-based, solutions are often highly suboptimal, producing results that may misinterpret the extracted information and consequently miss many network events. We propose a novel method for the extraction of various predefined types of network events from raw network flow data. The new method is based on analysis of computational properties of the event types as prescribed by their attributes in a given descriptive language. The corresponding events are then extracted with a supreme recall as compared to a respective event extraction part of an in-production intrusion detection system Camnep.

Tracing the Packet Loss between Different Nodes within Same Link at Particular Interval of Time

Realistic and accurate packet loss measurement of production traffic has been challenging, since the frequently-used active monitoring approaches using probe packets cannot capture the packet loss experienced by the traffic of individual user applications. In this paper, we present a new approach for the accurate measurement of the packet loss rate faced by nodes at same link at particular instant of time. In contrast to previous work, our method is able to pinpoint the packet loss rate experienced by the individual traffic flows of concurrently running applications. Due to the exponential back-off scheme, one of these connections could gain an unfair advantage in channel access. If both the connections generate heavy loads, then the connection that began first will dominate the channel. If one of the connections is heavily loaded, while the other is lightly loaded, the heavily loaded connection dominates the channel.

Understanding Transportation Systems Through the Lenses of Experimental Economics: A Review

Planning, operation and analysis of transportation systems hinge on theories and assumptions regarding individual choices, traffic flows, and traffic equilibria. Transportation field data collection, however, often lacks sufficient control, and therefore is often not suitable to test theories in transport modelling and their underlying behavioural axioms. With increased pressure on transport planners to deliver efficient, safe and publicly acceptable plans, it is imperative for transport scientists to find alternatives when testing the behavioural axioms that underpin current theories. Transportation scientists have, in response, increasingly used methods from experimental economics to gain insights into behaviour as well as to test theories and policies. Here we review and critically analyse the use of the experimental method in transportation science. Specifically, we synthesize the findings of experimental studies with regard to safety, freight, route choice, departure time choice, and location choice. We also reflect on well-known traffic paradoxes. We find that some of the evidence for traffic paradoxes can be attributed to experimental artefacts and that experimental studies when conducted by engineers tend to lack dominance and when conducted by economists tend to lack salience. Dominance and salience are key precepts of the external validity of studies drawing on methods from experimental economics. This review work also highlights methods that strengthen dominance and salience when using experimental economics in transportation studies.

Modelling and Analysis of an Integrated Scheduling Scheme with Heterogeneous LRD and SRD Traffic

Multimedia applications in wireless networks are usually categorized into various classes according to their traffic patterns and differentiated Quality-of-Service (QoS) requirements. The traffic of heterogeneous multimedia applications often exhibits Long-Range Dependent (LRD) and Short-Range Dependent (SRD) properties, respectively. The integrated scheduling scheme that combines Priority Queuing (PQ) and Generalized Processor Sharing (GPS) within a hierarchical structure, referred to as PQ-GPS, has been identified as an efficient mechanism for QoS differentiation in wireless networks and attracted significant research efforts. However, due to the high complexity and interdependent relationship among traffic flows, modelling of the integrated scheduling scheme poses great challenges. To address this challenging and important research problem, we develop an original analytical model for PQ-GPS systems under heterogeneous LRD and SRD traffic. A cost-effective flow decomposition approach is proposed to equivalently divide the integrated scheduling system into a group of Single-Server Single-Queue (SSSQ) systems. The expressions for calculating the queue length distribution and loss probability of individual traffic flows are further derived. After validating its accuracy, the developed model is adopted as an efficient performance tool to investigate the important issues of resource allocation and call admission control in the integrated scheduling system under QoS constraints.

Modelling priority queuing systems with varying service capacity

Many studies have been conducted to investigate the performance of priority queuing (PQ) systems with constant service capacity. However, due to the time-varying nature of wireless channels in wireless communication networks, the service capacity of queuing systemsmay vary over time. Therefore, it is necessary to investigate the performance of PQ systems in the presence of varying service capacity. In addition, self-similar traffic has been discovered to be a ubiquitous phenomenon in various communication networks, which poses great challenges to performance modelling of scheduling systems due to its fractal-like nature. Therefore, this paper develops a flow-decomposition based approach to performance modelling of PQ systems subject to self-similar traffic and varying service capacity. It specifically proposes an analytical model to investigate queue length distributions of individual traffic flows. The validity and accuracy of the model is demonstrated via extensive simulation experiments.

Special issue: advanced topics on scalable computing

Special issue: advanced topics on scalable computing

Performance modelling and analysis of Deficit Round Robin scheduling scheme with self‐similar traffic

AbstractThe provisioning of fairness to various network applications is an important Quality‐of‐Service demand and has thus become a challenging research issue in contemporary communication networks. Deficit Round Robin (DRR) is a promising fair scheduling mechanism owing to its low complexity and excellent ability of achieving a good degree of fairness in terms of throughput. On the other hand, self‐similar traffic was found to be pervasively present in communication networks and has great impact on the performance of scheduling systems. However, to the best of our knowledge, there has not been any analytical model reported in the open literature for the DRR mechanism in the presence of self‐similar traffic. To fill this gap, this paper analytically investigates the queueing performance of DRR and develops a new analytical model for deriving the upper and lower bounds of the queue length distributions of individual traffic flows in DRR scheduling systems subject to self‐similar traffic. Extensive comparison between simulation and analytical results validates the accuracy of the developed model. To demonstrate its applications, the analytical model is used to investigate the effects of packet size on the performance of the queueing system. The developed model is further applied to study the configuration of weights of individual traffic flows. Copyright © 2010 John Wiley & Sons, Ltd.

Analytical modeling of an integrated priority and WFQ scheduling scheme in multi-service networks

The provisioning of differentiated Quality-of-Service (QoS) is a key objective in the design and deployment of convergent multi-service communication networks. Integrated traffic management schemes that combine various fundamental scheduling principles have been proposed for the QoS differentiation in cellular and IEEE 802.16/WiMAX-based broadband wireless networks. In this paper, cost-effective analytical performance models are devised for a new hybrid scheduling scheme based on the integration of the well-known Priority Queueing (PQ) and Weighted Fair Queueing (WFQ) scheduling principles. In these models, traffic arrival processes are represented, respectively, by non-bursty Poisson process and bursty-correlated Markov-Modulate Poisson Process (MMPP) and new closed-form expressions of main performance metrics are derived including system fairness, mean number of packets in each buffer, system throughput, mean queueing delay and packet loss probability of individual traffic flows. The accuracy of the analytical models is validated through extensive comparisons between analytical results and those obtained from simulation experiments. Finally, these models are adopted to investigate the impact of the weights of traffic flows served by WFQ and assess the adverse effects of the traffic burstiness and correlation on the performance of the integrated scheduling scheme.

Monitoring individual traffic flows within the ATLAS TDAQ network

The ATLAS data acquisition system consists of four different networks interconnecting up to 2000 processors using up to 200 edge switches and five multi-blade chassis devices. The architecture of the system has been described in [1] and its operational model in [2]. Classical, SNMP-based, network monitoring provides statistics on aggregate traffic, but for performance monitoring and troubleshooting purposes there was an imperative need to identify and quantify single traffic flows. sFlow [3] is an industry standard based on statistical sampling which attempts to provide a solution to this.Due to the size of the ATLAS network, the collection and analysis of the sFlow data from all devices generates a data handling problem of its own. This paper describes how this problem is addressed by making it possible to collect and store data either centrally or distributed according to need. The methods used to present the results in a relevant fashion for system analysts are discussed and we explore the possibilities and limitations of this diagnostic tool, giving an example of its use in solving system problems that arise during the ATLAS data taking.

Analytical queue length distributions of GPS systems with long range dependent service capacity

Generalized Processor Sharing (GPS) is an idealized scheduling mechanism with given weights assigned to individual traffic flows for service differentiation. Based on the principle of GPS, a number of variants have been proposed and deployed in real-world communication systems where the service capacity of network channels is usually variable. This paper proposes an analytical performance model for GPS systems with the variable service capacity characterized by Long Range Dependent (LRD) processes. The model is able to accommodate heterogeneous fractional Brownian motion (fBm) and Poisson traffic in multi-service networks. We derive the expressions of the queue length distributions of the GPS system and individual traffic flows. The accuracy of the analytical model is validated through comparison between analytical and simulation results.

Modelling and Analysis of Priority Queueing Systems with Multi-Class Self-Similar Network Traffic: A Novel and Efficient Queue-Decomposition Approach

Traffic self-similarity has been discovered to be a ubiquitous phenomenon in communication networks and multimedia systems. Due to its fractal-like nature, performance modelling of self-similar traffic poses greater challenges and exhibits more complexity than that of traditional non-bursty traffic. As a result, most existing studies on analytical modelling of priority queueing systems with self-similar inputs have been restricted to a simplified scenario where only two traffic flows are taken into account. This study contributes to performance modelling of priority queueing systems by proposing a novel queue-decomposition approach, which offers several potential advantages and unique innovations. Specifically, we extend the well-known empty buffer approximation (EBA) method to model priority queueing systems with multiple traffic flows and decompose the original priority queueing system into a group of single-server single-queue systems, which can make the challenging performance modelling problem tractable. We further develop an analytical model to investigate the queue length distributions of individual traffic flows. The validity and accuracy of the model demonstrated through extensive simulation experiments make it a practical and cost-effective evaluation tool for quantitatively evaluating the performance behavior of priority queueing systems with multi-class self-similar traffic under various working conditions.

Performance modelling of generalized processor sharing systems with multiple self-similar traffic flows

The Generalized Processor Sharing (GPS) is an important service differentiation scheme in telecommunication systems and has attracted great research interests due to the desirable properties of fairness, traffic isolation, and work conservation. The major limitation of the existing analytical performance models of GPS systems subject to self-similar traffic is that these models have been restricted to a simplified scenario with only two traffic flows. To overcome such a limitation, this study proposes a new flow-decomposition approach to modelling GPS systems subject to multiple self-similar traffic flows. We present and prove the existence of a feasible ordering of GPS systems based on the required and guaranteed service rates of traffic flows. With the aid of the feasible ordering, we decompose the GPS system into a group of single-queue systems and then derive the queue length distributions and loss probabilities of individual self-similar traffic flows in the original GPS system. Extensive simulation experiments are conducted to validate the accuracy of the developed analytical model.

Estimating TCP Packet Loss Ratio from Sampled ACK Packets

The advent of various quality-sensitive applications has greatly changed the requirements for IP network management and made the monitoring of individual traffic flows more important. Since the processing costs of per-flow quality monitoring are high, especially in high-speed backbone links, packet sampling techniques have been attracting considerable attention. Existing sampling techniques, such as those used in Sampled NetFlow and sFlow, however, focus on the monitoring of traffic volume, and there has been little discussion of the monitoring of such quality indexes as packet loss ratio. In this paper we propose a method for estimating, from sampled packets, packet loss ratios in individual TCP sessions. It detects packet loss events by monitoring duplicate ACK events raised by each TCP receiver. Because sampling reveals only a portion of the actual packet loss, the actual packet loss ratio is estimated statistically. Simulation results show that the proposed method can estimate the TCP packet loss ratio accurately from a 10% sampling of packets.