Optimal Traffic Routing Research Articles

An Adaptive Data Traffic Control Scheme with Load Balancing in a Wireless Network

The symmetric wireless network has been expected to be a revolutionary technology for mobile communications. Due to the limited resources of the microbase stations in the wireless network, the way to jointly optimize resource allocation, traffic throughput, latency, and other key performances is a hot research issue. In this paper, we introduce a joint optimization algorithm for improving the performance and balancing the traffic load of the wireless network. For the optimal traffic routing scheme, we transfer the problem to a mixed mathematical programming model. The model contains multiple traffic constraints and a single joint objective; the objective of the joint optimization are data transmission latency, energy consumption of wireless microbase stations, and throughput of links. Moreover, in order to approximately solve the optimization problem, we propose an efficient heuristic traffic transmission and migration scheme with load balancing, called an adaptive data traffic control scheme. The main idea of the proposed scheme is to split the traffic of overloaded microbase stations and links in the symmetric wireless network, so as to achieve load balancing and reduce the energy consumption of microbase stations. At last, the evaluations and simulations verify the proposed algorithm can efficiently optimize the energy allocation of microbase stations, and the network lifetime is increased to 210 rounds. Meanwhile, the network latency is reduced to 2–3 ms, and the network throughput is increased to 1000 Mb in our simulation environment. The constructed traffic control system for the traffic engineering-based wireless network in this paper can serve the intelligent system in the future.

Optimal traffic routing in the network virtualization context

SummaryNetwork virtualization enables service providers to instantiate virtual networks on a common physical infrastructure. Virtual networks are then allocated to clients in order to deploy or test their services. Thus, clients do not know how their traffic is routed, which server is responding, and which path is crossed. Service providers who own the physical infrastructure aim to share the load among multiple servers for traffic routing. However, those who do not own the infrastructure and want to lease it essay to concentrate all the traffic on one server and exactly the closest server. In this paper, we model the problems of load sharing on multiple servers and traffic concentration on one server as two mathematical programs. The objective functions correspond to service providers' aims under a delay constraint based on the M/D/1/N queue's mean sojourn time's formula. Due to its hardness, we provide an approximation of the delay constraint at low and medium traffic intensities. The approximation is analytically proved by the Taylor series development at a chosen point. Finally, through simulation and analytical models, we compare the performance of programs' optimal solutions. We notice that load sharing decreases the packet loss and jitter values. However, traffic concentration reduces the latency.

A stochastic optimal control approach for real-time traffic routing considering demand uncertainties and travelers’ choice heterogeneity

This paper develops a theoretical approach to identify optimal traffic routing strategy for managing transportation systems. It obtains the optimal traffic diversion ratio to each route that can be achieved in real time through cutting-edge sensing and vehicle-infrastructure communication technologies. We minimize the expected total travel time of all travelers in the network by providing and updating routing advice (or incentives) to travelers in real time. The system-optimum traffic routing problem is modeled using the stochastic control approach where demand uncertainty and travelers’ heterogeneity are explicitly considered over time. The approach is generic in the sense that the optimal routing strategies can be achieved through various technologies, such as connected vehicle technologies, navigation systems, variable message signs, dynamic pricing, etc. For a two-route representative network, we use dynamic programming to derive and approximate the analytical solution of the optimal routing policy for each time interval. The optimal diversion ratio can be updated solely upon the traffic counts measured along the preferred route in real time. The general rule is, with a high probability, to minimize the congestion and keep the maximum flow performance on the preferred route from the beginning of the peak hours. Towards the end of the peak hours, the optimal policy would allow more intensive use of the preferred route resulting over-saturation, whereas keeping the minimal use of the alternative route. The analytical solution is validated and examined in a synthesized network and a real-world network in California. It is found that it consistently outperforms the deterministic solution, and its resultant system performance is also reasonably close to the benchmark system optimum where true demand could be precisely known one day ahead.

Efficient Negative Cycle–Canceling Algorithm for Finding the Optimal Traffic Routing for Network Evacuation with Nonuniform Threats

A new network flow solution method is designed to determine optimal traffic routing efficiently for the evacuation of networks with several threat zones and with nonuniform threat levels across zones. The objective is to minimize total exposure (as duration and severity) to the threat for all evacuees during the evacuation. The problem is formulated as a minimum cost dynamic flow problem coupled with traffic dynamic constraints. The traffic flow dynamic constraints are enforced by the well-known point queue and spatial queue models in a time-expanded network presentation. The key to the efficiency of the proposed method is that, for any feasible solution, the algorithm can find and can cancel multiple negative cycles (including the cycle with the largest negative cost) with a single shortest path calculation made possible by applying a proposed transformation to the original problem. A cost transformation function and a multisource shortest path algorithm are proposed to facilitate the efficient detection and cancelation of negative cycles. Zone by zone, negative cycles are canceled at the border links of the zones. The solution method is proved to be optimal. The algorithm is implemented, tested, and verified to be optimal for a midsized example problem.

Optimal traffic routing strategy based on community structure

It is shown that community structure has great influence on traffic transportation. Networks with pronounced community structure are less efficient in terms of packet delivery. While the shortest path is chosen at random in the shortest path routing strategy, a routing strategy based on community structure is proposed in this paper which can reduce the betweenness centrality of the nodes on the edge of the community by minimizing the number of the communities that the shortest path passes through. Simulations show that the new strategy can enhance the packet delivery capability with the small-world character and that the more accurately the community is identified, the more efficient the new strategy is.

Q Value-Based Dynamic Programming with Boltzmann Distribution for Global Optimal Traffic Routing Strategy

In this paper, we propose a heuristic method -- Boltzmann Optimal Route Method trying to find a good approximation to the global optimum route for Origin-Destination pairs through iterations until the total traveling time converges. The overall idea of our method is to update the traveling time of each route section iteratively according to its corresponding traffic volume, and continuously generate a new global route by Q value-based Dynamic Programming combined with Boltzmann distribution. Finally, we can get the global optimum route considering the traffic volumes of the road sections. The new proposed method is compared with the conventional shortest-path method- Greedy strategy both in the static traffic system where the volumes of all the given Origin-Destination pairs of road networks are constant and in the dynamic traffic system in which changing traffic volumes are constantly provided. The results demonstrate that the proposed method performs better than the conventional method in global perspective.

Optimal traffic routing strategy on scale-free complex networks

In this paper, we propose a new routing strategy to improve the transportation performance on scale-free networks, named optimal routing strategy. It can proportionally distribute the traffic load between central nodes and the noncentral nodes. Analytical results indicate that by using the optimal routing strategy, the network capability in processing traffic is proportional to the square of the network size and is independent of each node degree. Simulations show that compared with the classic shortest path routing strategy, the new strategy can enhance the network capability several times with the small-world character and its performance is gradually improved with the increasing of the average degree. Moreover, the comparison with the efficient routing strategy also reveals the prominent performance of the new strategy.

Edge-based traffic engineering for OSPF networks

This paper proposes and evaluates a novel, edge-based approach, which we call the k-set Traffic Engineering (TE) method, to perform traffic engineering in OSPF networks by partitioning traffic into uneven k-traffic sets. The traffic partitioning and splitting takes place only at network edges, leaving the core simple. We theoretically prove that if k is large enough, the k-set TE method achieves the general optimal traffic engineering where full-mesh overlaying and arbitrary traffic splitting, such as in MPLS, have to be used. We give an upper bound of the smallest k that achieves such a general optimum. In addition, we provide a constant worst case performance bound if k is smaller than the optimal k. Finding the optimal traffic splitting and routing for a given k is NP-hard. Therefore, we present a heuristic algorithm to handle the problem. The performance of the k-set TE method together with the proposed heuristic algorithm is evaluated by simulation. The results confirm that a fairly small k (2 or 4) can achieve good near-optimal traffic engineering. Overall, the k-set TE method provides a simple and efficient solution to achieve load balancing in OSPF networks. It follows the “smart edge, simple core” design rule of the Internet. It is also able to keep “the same path for the same flow,” which is desirable and beneficial to TCP applications.

Traffic pattern based mobile routing scheme

This paper presents a novel routing scheme for wireless mobile networking which is adaptive to mobile traffic patterns. We propose a selective location information update scheme to accommodate the need for optimal traffic routing as well as minimizing network bandwidth consumption by routing updates. Performance analysis shows that this adaptive routing scheme is highly efficient over a wide range of traffic patterns.

Neural networks for routing communication traffic

The use of neural network computational algorithms to determine optimal traffic routing for communication networks is introduced. The routing problem requires choosing multilink paths for node-to-node traffic to minimize loss, which is represented by expected delay or some other function of traffic. The minimization procedure is implemented using a modification of the neural network traveling-salesman algorithm. Illustrative simulation results on a minicomputer show reasonable convergence in 250 iterations for a 16-node network with up to four links from origin to destination.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A hierarchical scheme for multiobjective adaptive routing in large communication networks

A novel two-level scheme for designing protocols for optimal traffic routing in large communication networks is presented. Major strong points of the scheme are: i) it is adaptive to changes in load and network topology, ii) it permits consideration of multiple objective functions in performance optimization, and iii) it combines elements of flow control and routing for an effective control of traffic congestion.