Traffic Engineering Problem Research Articles

Node-Constrained Traffic Engineering: Theory and Applications

Traffic engineering (TE) is a fundamental task in networking. Conventionally, traffic can take any path connecting the source and destination. Emerging technologies such as segment routing, however, use logical paths going through a predetermined set of middlepoints. Inspired by this, in this work we introduce the problem of node-constrained TE, where traffic must go through a set of middlepoints, and study its theoretical fundamentals. We show that the general node-constrained TE that constrains the traffic to take paths going through one or more middlepoints is NP-hard for directed graphs but strongly polynomial for undirected graphs, unveiling a profound dichotomy between the two cases. We additionally investigate the popular variant of node-constrained TE that uses shortest paths between middlepoints, and show that the problem can now be solved in weakly polynomial time for a fixed number of middlepoints. Yet if we constrain the end-to-end paths to be acyclic, the problem can become NP-hard. This explains why existing work focuses on the computationally tractable variant. An important application of our work concerns the computational complexity of flow centrality, first proposed in 1991 by Freeman et al. [21]: we show that it is NP-hard for directed but strongly polynomial for undirected graphs. Finally, we investigate the middlepoint selection problem in general node-constrained TE. We introduce group flow centrality as a solution concept for multi-commodity networks, study its complexity, and show that it is monotone but not submodular for both directed and undirected graphs. Our work provides a thorough theoretical treatment of node-constrained TE and its applications.

Service traffic engineering: Avoiding link overloads in service chains

Emerging technologies such as software defined networks (SDN) and network function virtualization (NFV) enable flexibility in operation and a significant reduction of the costly hardware deployment in networks and improve service agility too. Virtual network functions (VNF) are running as virtual machines on servers, and are organized in chains using service function chaining (SFC). Though many SFC algorithms were introduced, they mostly neglect the state of the underlying networking layers and infrastructure, considering static costs for the connections among the VNFs. In this paper, we apply a model that considers also the topology and current load information of the IP layer below. We formulate the problem using an integer linear program (ILP) and propose also a heuristic algorithm with the objective of avoiding link overloads in the networking layer. Thus, we solve the stated VNF-SFC problem similar to the basic traffic engineering (TE) problem in networks. We define different architectures to classify the solutions according to the amount of networking layer information applied in VNF-SFC solutions. We analyze the operation of the proposed solutions and show their benefits and drawbacks by numerical results of a case study.

Enhancing Video Rate Adaptation With Mobile Edge Computing and Caching in Software-Defined Mobile Networks

Recent advances in software-defined mobile networks (SDMNs), in-network caching, and mobile edge computing (MEC) can have significant effects on video services in next generation mobile networks. In this paper, we jointly consider SDMNs, in-network caching, and MEC to enhance the video service in next generation mobile networks. We use a new video experience evaluation standard called U-video mean opinion score (vMOS), which is a more advanced measurement of the video quality based on the well-known vMOS. With the objective of maximizing the mean U-vMOS, an optimization problem is formulated. Due to the coupling of video data rate, computing resource, and traffic engineering (bandwidth provisioning and paths selection), the problem becomes intractable in practice. Thus, we utilize a dual-decomposition method to decouple those three sets of variables. By this decoupling, video rate adaptation is performed at users with network assistants. End nodes can schedule computing resource independently. Traffic engineering is performed by the software-defined networking controller and base stations. Furthermore, to address the challenges of dynamic change of network status and the drawbacks caused by the frequent exchange of information, we design a decentralized algorithm based on alternating direction method of multipliers to solve the traffic engineering problem. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.

A Distributed Semiasynchronous Algorithm for Network Traffic Engineering

In this paper, we consider the traffic engineering problem in a large-scale hierarchical network arising in the next-generation cloud-based wireless networks. We propose a distributed semiasynchronous algorithm for this problem based on the so-called block successive upper bound minimization method of multipliers (BSUM-M). Theoretically, we show that the proposed algorithm converges to the global optimal solution under some assumptions on the degree of network asynchrony. We illustrate the effectiveness and efficiency of the proposed algorithm by comparing it with the state-of-the-art commercial solvers in a networked environment.

Setting an Optimization Problem of Distributing Traffic Across Multiple Independent Paths

There appeared a need for developing a communication network for urgent need, which became world demand. The given article addresses at the solution of the problem of traffic engineering, using the mechanisms of multi-path routing within the developed algorithm, the optimal number of paths was defined. The proposed algorithm includes a multicriteria optimization procedure. The values of the maximum flow and multi-path delay are used as partial criteria. If necessary, a set of particular criteria can be extended. This allows to solve the problem of ensuring the required quality of service.

Traffic engineering in hybrid SDN networks with multiple traffic matrices

Traffic engineering (TE) is an efficient tool for optimizing traffic routing and balancing the flows in networks. Traffic is dynamic, previous TE optimization over a single traffic matrix (TM) have some limitations, because a single TM can have big measurement errors and is insufficient to depict the traffic flucuations. Thus, we consider using multiple TMs to overcome these limitations. With the emergence of Software Defined Networking (SDN), we can route flows more flexibly and better balance the flows over multiple TMs. However, due to the difficulties of full SDN deployment, hybrid SDN networks will be the prevailing architectures in the near future. Therefore, optimizing the routing over multiple TMs in a hybrid SDN network is of great interest.In this paper, we first formulate the problem of TE over multiple TMs and prove its NP-hardness. Next, we propose a heuristic algorithm for optimizing routing over multiple TMs by combining offline weight setting optimization with online splitting ratio optimization. Furthermore, we prove that the routes obtained in our algorithm are loop-free, and we provide an upper and lower bound of our proposed algorithm. Finally, we evaluate our method on measured traffic datasets with three network topologies. The results of extensive experiments demonstrate that the maximum link utilization of a network can be optimized better using our proposed algorithm.

Efficient traffic engineering for 5g core and backhaul networks

The next generation mobile networks (5G) should be efficient and elastic to accommodate numerous and diverse ser- vices. By explicitly assigning bandwidth to service flows, traffic engineering (TE) is effective to improve network efficiency and elasticity. Unfortunately, existing mobile network TE schemes are mostly focused on core network only, which is inadequate for effi- cient end-to-end traffic delivery in mobile networks. In this paper, we propose a TE framework that incorporates the data gateway (D-GW) selection and exploits the topology and traffic informa- tion of both core and backhaul networks for SDN-based 5G net- works. With ideal flow to D-GW association (IFDA) strategy, we formulate the TE problem as a multicommodity flow problem to achieve network load balancing. Considering the cooperation sig- nalling between D-GWs, we propose multiple BSs to one D-GW association (MBODA) and multiple flows to one D-GW associa- tion (MFODA) strategy, and formulate the corresponding TE prob- lems as mixed integer linear programs (MILPs) which are NP- hard. To efficiently solve the IFDA-TE problem, we design an im- proved version of fully polynomial time approximation scheme (i- FPTAS). Moreover, we propose a heuristic method and an LP re- laxation method that both use i-FPTAS to solve the MBODA-TE and MFODA-TE problems respectively. Numerical results show that i-FPTAS achieves close-optimal solution with significantly lower computational complexity, compared with FPTAS, and the performance of MFODA-TE is very close to that of the IFDA-TE, while there is a small performance degradation for MBODA-TE as the cost of computational efficiency.

Organizing Multipath Routing in Cloud Computing Environments

One of the objectives of organizing cloud systems is to ensure effective access to remote resources by optimizing traffic engineering (TE) procedures. This paper considers the traffic engineering problem in a cloud environment by using a multipath routing technique. The multipath routing algorithm is used to identify the maximum number of disjoint paths in the graph which overcomes the problem in the junction area estimation process. So, the algorithm forms a plurality of non-overlapping and partially intersecting paths between any two nodes is proposed. Finally, the conditions for the formation of multipath virtual channels to ensure minimum build-time posts for the parallel transmission of its parts are also discussed.

QoS-Aware Approach for Maximizing Rerouting Traffic in IP Networks

Network resilience provides an effective way to overcome the problem of network failure and is crucial to Internet protocol (IP) network management. As one of the main challenges in network resilience, recovering from link failure is important to maintain the constancy of packets being transmitted. However, existing failure recovery approaches do not handle the traffic engineering problem (e.g., tuning the routing-protocol parameters to optimize the rerouting traffic flow), which may cause serious congestions. Moreover, as the lack of QoS (quality of service) restrictions may lead to invalid rerouting traffic, the QoS requirements (e.g., bandwidth and delay) should also be taken into account when recovering the failed links. In this paper, we first develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures, with which we can choose reliable backup paths (BPs). Then we construct a mathematical model for the failure recovery problem, which takes maximum rerouting traffic as the optimizing objective and the QoS requirements as the constraints. Moreover, we propose a heuristic algorithm for link failure recovery, which adopts the improved k shortest path algorithm to splice the single BP and supplies more protection resources for the links with higher priority. We also prove the correctness of the proposed algorithm. Moreover, the time and space complexity are also analyzed. Simulation results under NS2 show that the proposed algorithm improves the link failure recovery rate and increases the QoS satisfaction rate significantly.

Optimal design of switched Ethernet networks implementing the Multiple Spanning Tree Protocol

Switched Ethernet networks rely on the Spanning Tree Protocol (STP) to ensure a cycle-free connectivity between nodes, by reducing the topology of the network to a spanning tree. The Multiple Spanning Tree Protocol (MSTP) allows for the providers to partition the traffic in the network and assign it to different virtual local area networks, each satisfying the STP. In this manner, it is possible to make a more efficient use of the physical resources in the network. In this paper, we consider the traffic engineering problem of finding optimal designs of switched Ethernet networks implementing the MSTP, such that the worst-case link utilization is minimized. We show that this problem is NP-hard. We propose three mixed-integer linear programming formulations for this problem. Through a large set of computational experiments, we compare the performance of these formulations. Until now, the problem was almost exclusively solved with heuristics. Our objective here is providing a first comparison of different models that can be used in exact methods.

Energy-aware traffic engineering in hybrid SDN/IP backbone networks

Software Defined Networking (SDN) can effectively improve the performance of traffic engineering and has promising application foreground in backbone networks. Therefore, new energy saving schemes must take SDN into account, which is extremely important considering the rapidly increasing energy consumption from Telecom and ISP networks. At the same time, the introduction of SDN in a current network must be incremental in most cases, for both technical and economic reasons. During this period, operators have to manage hybrid networks, where SDN and traditional protocols coexist. In this paper, we study the energy efficient traffic engineering problem in hybrid SDN/IP networks. We first formulate the mathematic optimization model considering SDN/IP hybrid routing mode. As the problem is NP-hard, we propose the fast heuristic algorithm named HEATE (Hybrid Energy-Aware Traffic Engineering). In our proposed HEATE algorithm, the IP routers perform the shortest path routing using the distribute OSPF link weight optimization. The SDNs perform the multi-path routing with traffic flow splitting by the global SDN controller. The HEATE algorithm finds the optimal setting of OSPF link weight and splitting ratio of SDNs. Thus traffic flow is aggregated onto partial links and the underutilized links can be turned off to save energy. By computer simulation results, we show that our algorithm has a significant improvement in energy efficiency in hybrid SDN/IP networks.

Review of Traffic Signal Control based on Fuzzy Logic

the present time the number of private vehicles is increasing rapidly. As result the traffic congestion is growing and becoming a huge problem in big cities. Due to traffic congestion many huge problems are occurring like pollution, wastage of time, money, accidents etc. Traffic congestion occurred very frequently in the present time which affect the daily life and introduce so many problems and challenges. To solve all these problems traffic signal control is a best solution. Fuzzy set theory have been widely applied to many applications and it can easily deal with uncertainty, complexity and imprecision in many systems. The problems in traffic engineering are mainly characterized by the imprecise, ambiguous and uncertain parameters. Due to such type of parameters fuzzy logic method is best suitable approach for traffic signal control. The performance of fuzzy logic based controller is better for two one way street based on extension of green light time. Later fuzzy logic controller was developed for two way intersection without turns, single intersection with all movements, multiple intersection etc. Better performance of fuzzy logic based traffic signal control in comparison of traditional traffic signal control increase the research in this field. Fuzzy logic approach will definitely help in transportation management system in future. Keywordsraffic signal control, fuzzy logic

Quasi-optimal feedback control for an isolated intersection under oversaturation

How to manage signalized intersections under oversaturated conditions is a long-standing problem in traffic science and engineering. However, although research works in this area date back to 1960s, an on-line control strategy with theoretically bounded performance is missing, even for the control of an isolated intersection under oversaturation. This paper makes one step further in this area by proposing a QUEUE-based quasi-optimal feedback control (abbreviated as QUEUE) strategy for an isolated oversaturated intersection. The QUEUE strategy is intuitive, simple, and proved to match the off-line optimum in the case of constant demand. More importantly, the bounds of sub-optimality of the QUEUE strategy can be specified quantitatively in general piece-wise constant demand cases. To better deal with the maximum queue constraints, the oversaturation period is divided into the queuing period and the dissipation period with two different objectives. In the queuing period, the primary objective is to keep the queue length within the maximum value; but for the dissipation period, the primary objective is to eliminate all queues at the earliest time. Interestingly, we found that both control objectives can be realized with the same QUEUE strategy. Numerical examples show that the QUEUE strategy approximates the off-line optimum very well. The average sub-optimality in comparison with the off-line optimum in the challenging conditions with Poisson distributed random demand is below 5%.

Modeling Left-Turn Driving Behavior at Signalized Intersections with Mixed Traffic Conditions

In many developing countries, mixed traffic is the most common type of urban transportation; traffic of this type faces many major problems in traffic engineering, such as conflicts, inefficiency, and security issues. This paper focuses on the traffic engineering concerns on the driving behavior of left-turning vehicles caused by different degrees of pedestrian violations. The traffic characteristics of left-turning vehicles and pedestrians in the affected region at a signalized intersection were analyzed and a cellular-automata-based “following-conflict” driving behavior model that mainly addresses four basic behavior modes was proposed to study the conflict and behavior mechanisms of left-turning vehicles by mathematic methodologies. Four basic driving behavior modes were reproduced in computer simulations, and a logit model of the behavior mode choice was also developed to analyze the relative share of each behavior mode. Finally, the microscopic characteristics of driving behaviors and the macroscopic parameters of traffic flow in the affected region were all determined. These data are important reference for geometry and capacity design for signalized intersections. The simulation results show that the proposed models are valid and can be used to represent the behavior of left-turning vehicles in the case of conflicts with illegally crossing pedestrians. These results will have potential applications on improving traffic safety and traffic capacity at signalized intersections with mixed traffic conditions.

Achieving Optimal Traffic Engineering Using a Generalized Routing Framework

The open shortest path first (OSPF) protocol has been widely applied to intra-domain routing in today’s Internet. Since a router running OSPF distributes traffic uniformly over equal-cost multi-path (ECMP), the OSPF-based optimal traffic engineering (TE) problem (i.e., deriving optimal link weights for a given traffic demand) is computationally intractable for large-scale networks. Therefore, many studies resort to multi-protocol label switching (MPLS) based approaches to solve the optimal TE problem. In this paper we present a generalized routing framework to realize the optimal TE, which can be potentially implemented via OSPF- or MPLS-based approaches. We start with viewing the conventional optimal TE problem in a fresh way, i.e., optimally allocating the residual capacity to every link. Then we make a generalization of network utility maximization (NUM) to close this problem, where the network operator is associated with a utility function of the residual capacity to be maximized. We demonstrate that under this framework, the optimal routes resulting from the optimal TE are also the shortest paths in terms of a set of non-negative link weights that are explicitly determined by the optimal residual capacity and the objective function. The network entropy maximization theory is employed to enable routers to exponentially, instead of uniformly, split traffic over ECMP. The shortest-path penalizing exponential flow-splitting (SPEF) is designed as a link-state protocol with hop-by-hop forwarding to implement our theoretical findings. An alternative MPLS-based implementation is also discussed here. Numerical simulation results have demonstrated the effectiveness of the proposed framework as well as SPEF.

Lexicographical minimization of routing hops in hop-constrained node survivable networks

In this paper, we address a hop-constrained node survivable traffic engineering problem in the context of packet switched networks with source based routing. Consider a telecommunications network with given link capacities that was dimensioned for a set of commodities, with estimated demand values, such that each commodity demand is routed through a set of node disjoint service and backup paths, all with at most H hops. When the network is put in operation, the real demand values might be different from the initial estimated ones. So, we aim to determine a set of hop-constrained service and backup paths for each commodity, with known demand values, such that the whole set of paths does not violate the link capacities. The traffic engineering goal is related with the hop minimization of only the service paths. We aim to minimize the number of routing hops in a lexicographical sense, i.e., minimize the number of service paths with the worst number of hops; then, among all such solutions, minimize the number of service paths with the second worst number of hops; and so on. We address two traffic engineering variants: in the first variant, all service paths of each commodity are accounted for in the objective function while in the second variant only the worst service path of each commodity is accounted for in the objective function. We first present and discuss three classes of Integer Linear Programming hop-indexed models--disaggregated, mixed and aggregated--for both variants. Then, we prove that, although the three classes are not equivalent, they provide the same Linear Programming relaxation bounds for each variant. Finally, we present computational results showing that, as a consequence, the more compact aggregated models are more efficient in obtaining the optimal integer solutions.

Quasi-optimal feedback control for a system of oversaturated intersections

Oversaturated intersection control is a long-standing problem in traffic science and engineering. The problem becomes even harder when we consider a system of oversaturated intersections. Most of the research works in this area are off-line studies that require fully knowledge of origin–destination demand, which would be difficult to obtain in reality. Although several on-line feedback control methods are proposed, they only aim at preventing queue spillover, not able to minimize vehicular delay time. Moreover, these on-line control strategies are not theoretically evaluated how optimal (or sub-optimal) they are. We propose in this paper a quasi-optimal decentralized QUEUE-based feedback (abbreviated as QUEUE) control strategy for a system of oversaturated intersections. The QUEUE strategy is applied cycle-by-cycle based on measurement of current queue sizes, but its overall result is able to approximate the optimal one derived from off-line studies. Details of the feedback control laws for upstream and downstream intersections, in the queueing period and the queue dissipation period, are discussed. Superior to the existing feedback control strategies, the upper bounds of sub-optimality of the QUEUE strategy generating from demand fluctuation and coupling of intersections are specified quantitatively. It is also theoretically proved that the queue measurement error or demand estimation error would not be amplified by the QUEUE strategy. Numerical examples show that the QUEUE strategy performs very well and is robust to errors.

Multi-criteria optimization of traffic signals: Mobility, safety, and environment

Two-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.

RESEARCH OF ENGINEERING TRAFFIC IN COMPUTER UZ NETWORK USING MPLS TE TECHNOLOGY

Purpose. In railway transport of Ukraine one requires the use of computer networks of different technologies: Ethernet, Token Bus, Token Ring, FDDI and others. In combined computer networks on the railway transport it is necessary to use packet switching technology in multiprotocol networks MPLS (MultiProtocol Label Switching) more effectively. They are based on the use of tags. Packet network must transmit different types of traffic with a given quality of service. The purpose of the research is development a methodology for determining the sequence of destination flows for the considered fragment of computer network of UZ. Methodology. When optimizing traffic management in MPLS networks has the important role of technology traffic engineering (Traffic Engineering, TE). The main mechanism of TE in MPLS is the use of unidirectional tunnels (MPLS TE tunnel) to specify the path of the specified traffic. The mathematical model of the problem of traffic engineering in computer network of UZ technology MPLS TE was made. Computer UZ network is represented with the directed graph, their vertices are routers of computer network, and each arc simulates communication between nodes. As an optimization criterion serves the minimum value of the maximum utilization of the TE-tunnel. Findings. The six options destination flows were determined; rational sequence of flows was found, at which the maximum utilization of TE-tunnels considered a simplified fragment of a computer UZ network does not exceed 0.5. Originality. The method of solving the problem of traffic engineering in Multiprotocol network UZ technology MPLS TE was proposed; for different classes its own way is laid, depending on the bandwidth and channel loading. Practical value. Ability to determine the values of the maximum coefficient of use of TE-tunnels in computer UZ networks based on developed software model «TraffEng». The input parameters of the model: number of routers, channel capacity, the number of streams, the average flow velocity. As a result of the model «TraffEng» choices variants of TE-tunnels with the corresponding values of the maximum coefficient of their use are presented.

Time-Headway Distributions on an Expressway: Case of Bangkok

Traffic flow modeling is one of the fundamental keys to solving a traffic engineering problem. Among many parameters, time headway is frequently used to model traffic flow characteristics. A statistical analysis of time headways is immensely important to both theoretical traffic modeling and simulation-based traffic modeling. Basically, it allows researchers to describe an inherently random pattern of traffic flows. Past studies have mainly focused on the time headways of vehicles on highways, freeways, and arterials. However, studies of time headways on urban expressways are rather limited and still need further investigation. In this paper, the author investigates and characterizes the time-headway distributions of vehicles traveling on an urban expressway in Bangkok, Thailand. Particularly, the exponential distribution, the lognormal distribution, and the generalized extreme value (GEV) distribution are used to model the time headways. It is found that the GEV distribution is most effective in modeling time headways. In fact, the GEV distribution can describe more than 90% of the empirical distributions on most lanes and sections of the expressway. On the other hand, the exponential distribution is the least effective distribution. It can only describe the empirical distributions during the periods when the traffic is extremely light.