Flow Arrival Rate Research Articles

Distributed Resource Allocation and Flow Control Algorithms for mmWave IAB Networks

This paper presents a new distributed slot reservation frame-work for joint resource allocation and flow control in mmWave IAB networks. We derive the Dynamic Slot Reservation (DSR) algorithm from a novel approach to solve a minimum clearing time linear program in a completely distributed manner. The algorithm to solve this problem, the Static Slot Reservation (SSR) algorithm, is also a contribution of the paper. We compare the delay performance of the DSR algorithm with a well known optimal, centralized algorithm, the joint-MWM algorithm, for a realistic IAB network scenario of multi-hop flows. We show that flows that traverse several links have significantly lower delays under DSR than under the joint-MWM algorithm. This paper also provides an instantaneous rate control policy for IAB networks which changes flow rates based on the number of flows at each node in the network. The flow rates under this policy are the same as the steady-state flow rates achieved by the DSR algorithm. We prove that the proposed flow control policy provides stability for all flow arrival rate vectors that are achievable by any flow control policy. This paper provides distributed admission control policies to provide rate and/or latency guarantees to flows under dynamic scenarios with stochastic flow arrivals and changing access link rates.

Platoon dispersion model in the mixed traffic environment

To describe the phenomenon of platoon dispersion in the mixed traffic environment and provide a theoretical basis for signal timing optimization in the mixed traffic environment. Based on Robertson’s platoon dispersion model, this paper fully considers the characteristics of mixed traffic conditions, and models the platoon dispersion in the mixed traffic environment. Firstly, considering the influence of the percentage of non-motor vehicles on the platoon dispersion, the functional relationship between the percentage of non-motor vehicles and the parameters of the platoon dispersion model is derived, and the parameters of the Robertson platoon dispersion model are calibrated. Secondly, through the data collected from Zitai Road in the city of Kunming, the required model parameters are calculated, and the arrival flow rate distribution downstream of the intersection is obtained. Finally, according to the measured data, the proposed model is compared with the classical Robertson model. The results show that compared with the Robertson model, the average prediction mean square error of the proposed model is reduced by 5.89%, which can better describe the dispersion law of the mixed traffic under the influence of different percentage of non-motorized vehicles.

Forwarding containers to dry ports in congested logistic networks

This work investigates the impact of container arrival flow rate on the port surrounding network and proposes a mixed integer programming model to optimize their forwarding towards dry port destinations. A novel efficient model with limited network capacity and time period dependent travel cost function is proposed. The aim is to give a decision support for operational planning with limited capacity of both the terminal yard and the logistic network, with respect to the quantity of containers transferred per time unit. The model considers time dependent costs and traveling times to reduce congestion. As a further novel issue of the model, arc costs and traveling times change during specific time slots. More precisely, new linear functions are derived as tangents to the nonlinear convex components of a classical traveling time function proposed in the literature. The aim is to produce a light model which can be effectively used for macro planning of forwarding operations. The model is proved to be fast in real case instances also if compared to classical literature approaches. The model is used to study the container dispatching process in a terminal which is going to be the main Italian container terminal equipped to manage mega-ship traffic. Results obtained from real-size instances are reported. We tested the behavior of the model under different scenarios. Our tests confirmed model efficiency and its of supporting the management of peak events also by controlling shut-down time slots to lower congestion.

Deterministic capacity drop and morning commute in traffic corridor with tandem bottlenecks: A new manifestation of capacity expansion paradox

Deterministic capacity drop is a common phenomenon that occurs in traffic bottlenecks, and it has an intricate impact on the evolution of traffic flow in a traffic corridor with tandem bottlenecks. Considering the above facts, this study investigates deterministic capacity drop’s effects on commuter’s departure time choice, the evolution of traffic flow and traffic congestion in the morning commute process in a traffic corridor with tandem bottlenecks. We derive the closed-form solutions of the departure flow rate and arrival flow rate in different bottlenecks. Based on the formulations of these solutions, we analyze the influences of the deterministic capacity drop on commuter’s trip cost and the existence of different cases in the morning commute process in one origin one destination traffic corridor and Y-shape traffic corridor. A new manifestation of the capacity expansion paradox caused or influenced by the deterministic capacity drop is found. The result of this study reveals the essential role of the deterministic capacity drop in the morning commute process, and provides some advice for traffic control.

Modelling of Queue Length in Freeway Work Zones – Case Study Karaj-Tehran Freeway

In this study, the traffic parameters were collected from three work zones in Iran in order to evaluate the queue length in the work zones. The work zones were observed at peak and non-peak hours. The results showed that abrupt changes in Freeway Free Speed (FFS) and arrival flow rate caused shockwaves and created a bottleneck in that section of the freeway. In addition, acceleration reduction, abrupt change in the shockwave speed, abrupt change in the arrival flow rate and increase in the percentage of heavy vehicles have led to extreme queue lengths and delay. It has been found that using daily traffic data for scheduling the maintenance and rehabilitation projects could diminish the queue length and delay. Also, by determining the bypass for heavy vehicles, the delay can be significantly reduced; by more than three times. Finally, three models have been presented for estimating the queue length in freeway work zones. Moreover, the procedure shown for creating a queue length model can be used for similar freeways.

Cycle-Based Queue Length Estimation for Signalized Intersections Using Sparse Vehicle Trajectory Data

Intersection queue length estimation using high-resolution probe vehicle trajectory data has received increasing attentions in recent years. Existing methods for cycle-based queue length estimation still face the challenge of low and/or unstable estimation accuracies under the condition of sparse vehicle trajectory data, i.e., there is no greater than one vehicle trajectory per cycle on average. To address this challenge, this study proposed a novel approach for cycle-based queue length estimation by fusing real-time and historical probe vehicle trajectory data, through a statistical parameter estimation method, i.e., maximum likelihood estimation (MLE). With known signal timing information, firstly, the historical probe trajectory data are used to acquire the arrival flow rate distribution over the entire study period. Then, a likelihood function of queue length is derived by fully exploiting real-time traffic flow information provided by the queued and non-queued probe vehicles. Finally, the MLE method is adopted to estimate the cycle-based queue lengths with the maximum probability. The proposed approach is verified using both simulation and empirical data. Results indicate that precise estimation for cycle-based queue lengths can be realized based on sparse vehicle trajectory data, while showing superiority to a representative existing method. The proposed method is basically an offline method, but it can also work in an online manner if provided a priori arrival distribution either acquired from historical probe vehicle trajectory data or a theoretical assumption.

PopFlow: a novel flow management scheme for SDN switch of multiple flow tables based on flow popularity

Pipeline processing is applied to multiple flow tables (MFT) in the switch of software-defined network (SDN) to increase the throughput of the flows. However, the processing time of each flow increases as the size or number of flow tables gets larger. In this paper we propose a novel approach called PopFlow where a table keeping popular flow entries is located up front in the pipeline, and an express path is provided for the flow matching the table. A Markov model is employed for the selection of popular entries considering the match latency and match frequency, and Queuing theory is used to model the flow processing time of the existing MFT-based schemes and the proposed scheme. Computer simulation reveals that the proposed scheme substantially reduces the flow processing time compared to the existing schemes, and the difference gets more significant as the flow arrival rate increases.

A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

This paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.

Analysis of Policy-Based Security Management System in Software-Defined Networks

In software-defined networks, policy-based security management or architecture (PbSA) is an ideal way to dynamically control the network. We observe that on the one hand, this enables security capabilities intelligently and enhance fine-grained control over end user behavior. But, on the other hand, dynamic variations in network, rapid increases in security attacks, geographical distribution of nodes, complex heterogeneous networks, and so on have serious effects on the performance of PbSAs. These affect the flow specific quality of service requirements with further degradation of the performance of the security context. Hence, in this letter, PbSA’s performance is evaluated. The key factors including a number of rules, rule-table size, position of rules, flow arrival rate, and CPU utilization are examined, and found to have considerable impact on the performance of PbSAs.

High-Resolution Traffic Flow Prediction Model Based on Deep Learning

The traditional platoon dispersion model is based on the hypothesis of probability distribution, and the time resolution of the existing traffic flow prediction model is too big to be applied to the adaptive signal timing optimization. Based on the view of the platoon dispersion model, the relationship between vehicle arrival at downstream intersection and vehicle departure from the upstream intersection was analyzed. Then, the high-resolution traffic flow prediction model based on deep learning was proposed. The departure flow rate at the upstream was taking as the input and the arrival flow rate at downstream intersection was taking as the output in this model. Finally, the parameters of the proposed model were trained by the field survey data, and this model was implemented to predict the arrival flow rate of the downstream intersection. The result shows that the proposed model can better reflect the fluctuant characteristics of traffic flow and reduced the sum of the squared errors (SSE), MSE, and MAE by 13.17%, 13.21%, and 14.24%, compared with Robertson’s model. Thus, the proposed model can be applied for real-time adaptive signal timing optimization.

Maximum Remaining Service Time in Infinite-Server Queues

We study the maximum remaining service time in infinite-server queues of type M|G|∞ (at a given time and in a stationary regime). The following cases for the arrival flow rate are considered: (1) time-independent, (2) given by a function of time, (3) given by a random process. As examples of service time distributions, we consider exponential, hyperexponential, Pareto, and uniform distributions. In the case of a constant rate, we study effects that arise when the average service time is infinite (for power-law distribution tails). We find the extremal index of the sequence of maximum remaining service times. The results are extended to queues of type MX|G|∞, including those with dependent service times within a batch.

Controller-proxy: Scaling network management for large-scale SDN networks

Software defined networking (SDN) decouples the control and data plane of a network, which then drastically increases its flexibility and programmability. In large-scale networks such as data center networks, the scalability of a network is often limited by the SDN control plane because of a high flow arrival rate and uneven hotspot distribution. The objective of this paper is to design new mechanisms that utilize the computing resources in switches to scale up the SDN control plane without sacrificing the advantages of SDN. For this purpose, we propose the controller-proxy method, in which the SDN control plane can dynamically delegate a part of the event-processing logic to switches and utilize the computing resources in switches instead of handling OpenFlow events in the controllers. Experimental results show that controller-proxy method can reduce the workload of the control plane and improve the scalability of SDN networks by relieving the control path bottleneck.

Prediction of arrival flow profile of transit stream on link

Abstract It is essential in coordinated control to analyses the relationship of vehicular stream between upstream and downstream on arterial, and the arrival flow profile of section is a useful tool to describe the relationship. The arrival flow profile of transit stream is variable because of lower speed and stopping service. The objective of this paper is analyzing the relationship and characteristic of the transit stream, and predicting the arrival flow profile of transit stream on link. According the road conditions, some critical sections on arterial were defined. Simulation detected data were collected, the arriving vehicles at different signal periods were summarized and counted according to the smaller intervals, and the arrival flow profiles on critical sections were drawn. The impact of arrival flow profile by segment dispersion and stopping service was analyzed. On the basis of the speed cumulative probability distribution, transfer matrix which considers segment dispersion was built. The postpone time of transit vehicle was computed with Monte Carlo numerical simulation method. On the basis of postpone time cumulative frequency curve, postpone transition rate of interval was obtained by shifting the curve and calculating the areas, and the transfer matrix which considers stopping service was built. The arrival flow rate of upstream section multiplied by the transfer matrix made the arrival flow rate of downstream section on link. The results show that Monte Carlo numerical simulation method is feasible, and the relative error of predicted distribution of arrival flow rate is small. The prediction methods of arrival flow rate based on transfer matrix is effective.

A General QoS Aware Flow-Balancing and Resource Management Scheme in Distributed Software-Defined Networks

Due to the limited service capabilities of centralized controllers, it is difficult to process high volume of flows within reasonable time. This particularly degrades the strict quality of service (QoS) requirements of interactive media applications, which is non-negligible factor. To alleviate this concern, distributed deployments of software-defined network (SDN) controllers are inevitable and have gained a predominant position. However, to maintain application specific QoS requirements, the number of resources used in network directly impacts the capital and operational expenditure. Hence, in distributed SDN architectures, issues such as flow arrival rate, resources required and operational cost have significant mutual dependencies on each other. Therefore, it is essential to research feasible methods to maintain QoS and minimize resources provisioning cost. Motivated from this, we propose a solution in a distributed SDN architectures that provides flow-balancing (with guaranteed QoS) in pro-active operations of SDN controllers, and attempts to optimize the use of instance resources provisioning costs. We validate our solution using the tools of queuing theory. Our studies indicate that with our solution, a network with minimum resources and affordable cost with guaranteed application QoS can be set-up.

Performance Enhancement Architecture of VoIP Applications in Multiprotocol Label Switching Networks

In recent years, Voice over IP (VoIP) has impacted global telecommunications and networking tremendously. Traffic engineering and Quality of Service (QoS) guarantees for VoIP services pose a challenge for network researchers and designers. The repeated use of Internet Protocol shortest path towards the same destination may lead to unbalanced traffic situations and degraded network performance. Therefore, load balancing and link utilization become the critical functions in Internet Protocol routing for providing Quality of Service assurance for VoIP application. The aim of this work is to employ Multiprotocol Label Switching Network as a traffic engineering tool to enhance the QoS for VoIP applications. To achieve this, an effective Multiprotocol Label Switching Network load balancing architecture is developed that classifies the Internet traffic flows, routes the flows into multiple paths. Flow arrival rate, packet loss rate and delay are measured and taken as the input parameters and compared with the threshold values to identify the VoIP flow. Network load status is calculated by estimating the average buffer occupancy value and multipath routing is triggered when the network load is high to enhance the QoS. The investigated performance measures like throughput, delay and packet loss are reported to show the efficiency of the proposed technique for effective VoIP flows.

JumpFlow: Reducing flow table usage in software-defined networks

The forwarding scheme in Software-Defined Networking (SDN) is usually coupled with flow table management. To reduce the redundancy in the flow tables of OpenFlow switches, some recent studies propose forwarding flows using stacked MPLS labels, in which each label in the stack indicates the forwarding decision at one hop of the forwarding route. However, using multiple MPLS labels in each packet introduces significant transmission overhead, especially in networks with large diameters.In this paper, we propose JumpFlow, a forwarding scheme that achieves low and balanced flow table usage in an SDN by properly and reactively placing flow entries on switches. To reduce the transmission overhead, JumpFlow uses the available VLAN identifier (VID) in the packet header to carry routing information. Constrained by the limited space of the VID, a flow’s complete routing information must be divided into several sections and loaded separately at different switches on the flow’s forwarding route. To achieve low and balanced flow table usage, we formulate and solve the reactive flow entry placement problem. We evaluate JumpFlow against the per-hop configuration-based forwarding of OpenFlow for both unicast and multicast scenarios in a real network topology with different traffic patterns. For the unicast scenario with different new flow arrival rates, JumpFlow postpones the time when the first flow rejection occurs, reduces the flow rejection percentage by 37.06%, and reduces the control messages for route configuration by 53.52% on average. For the multicast scenario with a high new multicast group arrival rate, JumpFlow increases the ratio of accepted multicast groups by 83.90%, and reduces the ratio of average control messages for a multicast group configuration by 32.68%.

Three-phase theory of city traffic: Moving synchronized flow patterns in under-saturated city traffic at signals

Three-phase traffic flow theory of city traffic has been developed. Based on simulations of a stochastic microscopic traffic flow model, features of moving synchronized flow patterns (MSP) have been studied, which are responsible for a random time-delayed breakdown of a green-wave (GW) organized in a city. A possibility of GW control leading to the prevention of GW breakdown has been demonstrated. A diagram of traffic breakdown in under-saturated traffic (transition from under- to over-saturated city traffic) at the signal has been found; the diagram presents regions of the average arrival flow rate, within which traffic breakdown can occur, in dependence of parameters of the time-function of the arrival flow rate or/and signal parameters. Physical reasons for a crucial difference between results of classical theory of city traffic and three-phase theory are explained. In particular, we have found that under-saturated traffic at the signal can exist during a long time interval, when the average arrival flow rate is larger than the capacity of the classical theory; the classical capacity is equal to a minimum capacity in three-phase theory. Within a range of the average arrival flow rate between the minimum and maximum signal capacities, under-saturated traffic is in a metastable state with respect to traffic breakdown. We have distinguished the following possible causes for the metastability of under-saturated traffic: (i) The arrival flow rate during the green phase is larger than the saturation flow rate. (ii) The length of the upstream front of a queue at the signal is a finite value. (iii) The outflow rate from a MSP (the rate of MSP discharge) is larger than the saturation flow rate.

Scalable software defined network controllers

Software defined networking (SDN) introduces centralized controllers to dramatically increase network programmability. The simplicity of a logical centralized controller, however, can come at the cost of control-plane scalability. In this demo, we present McNettle, an extensible SDN control system whose control event processing throughput scales with the number of system CPU cores and which supports control algorithms requiring globally visible state changes occurring at flow arrival rates. Programmers extend McNettle by writing event handlers and background programs in a high-level functional programming language extended with shared state and memory transactions. We implement our framework in Haskell and leverage the multicore facilities of the Glasgow Haskell Compiler (GHC) and runtime system. Our implementation schedules event handlers, allocates memory, optimizes message parsing and serialization, and reduces system calls in order to optimize cache usage, OS processing, and runtime system overhead. Our experiments show that McNettle can serve up to 5000 switches using a single controller with 46 cores, achieving throughput of over 14 million flows per second, near-linear scaling up to 46 cores, and latency under 200 μs for light loads and 10 ms with loads consisting of up to 5000 switches.

Parameter estimation for product-form distributions of queueing networks

Basic parameters of a queueing network are its routing matrix, arrival flow rate, and service rates at network nodes. To estimate these parameters, one has to solve a system of balance equations. In turn, a product-form limiting distribution of the number of customers at the network nodes is defined through loading factors. Therefore, in the paper we propose to estimate loading factors through estimates of the limiting distribution based on observations of the number of customers at the nodes. This makes it possible to avoid solving a system of balance equations. This algorithm is realized for Jackson networks: classical, in a random environment, with blocked transitions.

Congestion at flow level and the impact of user behaviour

So-called elastic flows, corresponding to document transfers of various types, constitute the bulk of Internet traffic. This paper presents models of a single bottleneck link handling elastic traffic, accounting for random flow arrivals. The transport protocol and packet scheduling are taken into account approximately by assuming perfectly realized bandwidth sharing objectives. We refer to the demand as the product of the flow arrival rate and the average flow size. It is shown that per-flow throughput performance is generally satisfactory as long as demand is only slightly less than capacity. In overload, on the other hand, some flows must be abandoned. A fraction of link bandwidth is then wasted and performance critically depends on user behaviour. The models are useful in appraising the effectiveness of proposed schemes for Internet service differentiation.