Traffic Uncertainty Research Articles

Robust Optimization for OSPF Routing

Given a weight for each network link, which is set by the network operator, the OSPF routing protocol states that the data between each pair of nodes is routed through the shortest path between the sender and the receiver. In the case of multiple shortest paths, the traffic is split evenly, among all outgoing links that belong to the shortest paths. The OSPF weight setting problem consists in assigning the link weights such that the respective shortest path routing results in the least congested network. Most of the works in the literature assume that a single static traffic matrix is available. However, the traffic on computer networks may significantly vary in different periods of time, and it is not practical for the network operator to manually change the weights of the links each time significant variation in traffic occurs. These factors motivated the development of optimization models for OSPF weight setting that deal with traffic uncertainties. Instead of minimizing the average congestion over all scenarios as is the case of the works in the literature, we propose a new optimization models, based on Robust Optimization, where the congestion in each scenario is considered individually. We argue that the user experiences each scenario individually. Therefore, a solution that is good on average may sometimes result in a bad quality-of-service from the user point of view. Computational experiments show that, compared to the approach that minimizes the average case, our approach is able to reduce the congestion regret by 24.96%, while increasing the average congestion by only 0.72%, indicating that our approach may be a better alternative for weight setting in OSPF networks.

Hybrid Electromagnetism-Like Algorithm for Dynamic Supply Chain Network Design under Traffic Congestion and Uncertainty

With the constantly increasing pressure of the competitive environment, supply chain (SC) decision makers are forced to consider several aspects of business climate. More specifically, they should take into account the endogenous features (e.g., available means of transportation, and the variety of products) and exogenous criteria (e.g., the environmental uncertainty, and transportation system conditions). In this paper, a mixed integer nonlinear programming (MINLP) model for dynamic design of a supply chain network is proposed. In this model, multiple products and multiple transportation modes, the time value of money, traffic congestion, and both supply-side and demand-side uncertainties are considered. Due to the complexity of such models, conventional solution methods are not applicable; therefore, two hybrid Electromagnetism-Like Algorithms (EMA) are designed and discussed for tackling the problem. The numerical results show the applicability of the proposed model and the capabilities of the solution approaches to the MINLP problem.

Link Adaptation Strategies for Next Generation Satellite Video Broadcasting: A System Approach

A new era for satellite video broadcasting is dawning as a result of two major developments: 1) the video codec bit rates required by new ultrahigh definition TV standards are multiples of current high definition TV and 2) satellite operators turn to multi-mission multi-beam Ka -band architectures in order to address the traffic uncertainties of a highly diversified and unpredictable market that mixes broadband and broadcast services. To continue being the prime provider of video broadcasting, the next generation of satellite broadcasting systems should exploit advanced system and physical layer concepts. Along this line, this paper carries out a detailed assessment of link adaptation strategies employing sophisticated tools to simulate system and propagation effects beyond today’s constant coding and modulation paradigm. Specifically, the dimensioning of variable coding and modulation in a multibeam system, as well as the introduction of adaptive coding and modulation for broadcasting systems are addressed, and the strengths and weaknesses of each are highlighted through comprehensive simulations.

Energy Conservation Routing in Multihop Wireless Networks

Energy conservation is a central issue in the design of a wireless mesh network powered by solar cells. Since traffic uncertainty is always present, in this paper, we study how to design an energy-conservation routing scheme in face of traffic uncertainty. We present a new approach that is immune from the two problems prevalent in conventional approaches: performance degradation caused by inaccuracy of a simplified interference model and the instability caused by the assumption of unbounded transmission power and data rate. The analytical and simulation results presented in this paper show that the proposed approach can achieve significant performance gains, in terms of throughput and energy consumption, over the conventional approach.

Towards the fast and robust optimal design of wireless body area networks

Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.

Reducing the power consumption of hose‐model networks with bundled links

Given the exponential growth in telecommunication networks, more and more attention is being paid to their share in the global energy consumption. Reducing networks’ power consumption has become a major concern. However, the often over-provisioned wired core network is still overlooked. In core networks, pairs of routers are typically connected by multiple physical cables that form one logical bundled link participating in the intra-domain routing protocol. To reduce the energy consumption of core networks with bundled cables, the authors propose a scheme to deactivate the maximum possible number of cables, and associated equipment, while considering hose-model traffic. A similar approach has been presented for the pipe model, where the exact traffic matrix is assumed to be known. Owing to traffic uncertainty, however, it is difficult for network operators to have exact knowledge of the traffic matrix. This traffic uncertainty can be avoided by using the hose model, which specifies only the upper bounds of the egress/ingress traffic from/to a node. They introduce a mixed integer linear problem formulation that yields the optimal solution and a more practical and near optimal heuristic algorithm for large networks. The author's performance evaluation results show that it offers up to 50% power reduction.

Optimal coordination of variable speed limit to eliminate freeway wide moving jams

Variable speed limit (VSL) is well-known for the ability to eliminate freeway wide moving jam (WMJ). Different VSL maneuvers have been proposed and proved to be useful theoretically or empirically. However, the coordination of VSL still needs to be analyzed in order to cater for different traffic conditions encountered on the roadway. In this paper, we execute a study on freeway VSL method based on the three-phase traffic theory and Cellular Automata (CA) based simulations. Results indicate that VSL is a promising method in solving freeway WMJs, but in some cases, it induces new congestions due to improper configurations and the stochastic feature of traffic flow. Therefore, in this paper, we propose an optimal coordination model of VSL including five main principles. The purpose of our model lies on two aspects, to ensure the elimination of WMJ and to avoid the emergence of new traffic jams. By analyzing the traffic dynamics, we first present several theoretical principles of VSL configuration with respect to the WMJ elimination. Second, we study the relation between VSL effectiveness and the traffic uncertainty through CA model-based simulations and some empirical principles are raised.

Traffic uncertainty models in network planning

In network planning one designs the network and its routing policies based on a "guesstimate" of the amount of traffic that might be routed through the network in the future. Clearly, not being able to foretell the future means that one of the biggest challenges in network planning is how to deal with traffic uncertainty. If not solved appropriately, the network operator has to resort to overprovisioning, which is costly. On a smaller timescale, traffic that is being routed through the network is generally stochastic in nature, which is also reflected in dynamically varying network parameters like available bandwidth and delay of links. In this article, we provide an overview of several traffic uncertainty models useful for network planning. Our focus is on offline approaches to network planning and online routing in stochastic networks. We conclude with some possible directions for future work.

Advances in network planning - Part II: wireless networks and traffic uncertainty [Guest Editorial

Not only technologies, but also methodologies are evolving and changing the shape of network planning. This second part of this Feature Topic focuses on the technological side concerning wireless networks and the emerging methodologies to handle traffic uncertainty.

Принципи вдосконалення управління потоками повітряного руху та пропускною спроможністю в термінальних диспетчерських районах в умовах невизначеності

Розглянуто показники потоків повітряного руху, фактори невизначеності та управління потоками повітряного руху в термінальних диспетчерських районах. Запропоновано рекомендації щодо аеронавігаційної системи України.

Option Game Model for Optimizing Concession Length and Public Subsidies of Public–Private Partnerships

As public–private partnership (PPP) to address a significant funding shortage for transportation gains momentum in the United States, public agencies are also under pressure to demonstrate value for money and to deliver more for less on PPP deals. One critical challenge is how to optimize the concession length and amount of public subsidies so that a successful partnership can be established without undue sacrifice of the public interest. This paper presents an option game model for optimizing the concession length and public subsidy on a PPP project. The model integrates the flexibility of project expansion into the life-cycle analysis of project value, in addition to considering key factors, including subsidies, toll rate, and traffic uncertainty. The First Coast Outer Beltway project is used as an example to illustrate the process and merit of the option game model. In particular, by analyzing the trade-off between concession length and public subsidy, the paper provides new insights and implications for better PPP deal design.

Dynamic Coal Logistics Facility Location under Demand Uncertainty

In this paper, dynamic facility location and supply chain planning are studied through minimizing the costs of facility location, path selection and transportation of coal under demand uncertainty. The proposed model dynamically incorporates possible changes in transportation network, facility investment costs, operating cost and changes in facility location. In addition, the time variation and the demand uncertainty for coal in each period of the planning horizon is taken into account to determine the optimal facility location and the optimal production volumes. Computational process and results are presented for the model in a java language. Also, an empirical case study in Shanxi province is conducted in order to investigate the dynamic effects of traffic congestion and demand uncertainty on facility location design and total system costs.

Dynamic Network Design Problem under Demand Uncertainty: An Adjustable Robust Optimization Approach

This paper develops an adjustable robust optimization approach for a network design problem explicitly incorporating traffic dynamics and demand uncertainty. In particular, a cell transmission model based network design problem of linear programming type is considered to describe dynamic traffic flows, and a polyhedral uncertainty set is used to characterize the demand uncertainty. The major contribution of this paper is to formulate such an adjustable robust network design problem as a tractable linear programming model and justify the model which is less conservative by comparing its solution performance with the robust solution from the usual robust model. The numerical results using one network from the literature demonstrate the modeling advantage of the adjustable robust optimization and provided strategic managerial insights for enacting capacity expansion policies under demand uncertainty.

Robust delay-constrained routing in telecommunications

In telecommunications, operators usually use market surveys and statistical models to estimate traffic evolution in networks or to approximate queuing delay functions in routing strategies. Many research activities concentrated on handling traffic uncertainty in network design. Measurements on real world networks have shown significant errors in delay approximations, leading to weak management decisions in network planning. In this work, we introduce elements of robust optimization theory for delay modeling in routing problems. Different types of data uncertainty are considered and linked to corresponding robust models.

Robust network design: Formulations, valid inequalities, and computations

AbstractTraffic in communication networks fluctuates heavily over time. Thus, to avoid capacity bottlenecks, operators highly overestimate the traffic volume during network planning. In this article we consider telecommunication network design under traffic uncertainty, adapting the robust optimization approach of Bertsimas and Sim [Oper Res 52 (2004), 35–53]. We present two different mathematical formulations for this problem, provide valid inequalities, study the computational implications, and evaluate the realized robustness. To enhance the performance of the mixed‐integer programming solver, we derive robust cutset inequalities generalizing their deterministic counterparts. Instead of a single cutset inequality for every network cut, we derive multiple valid inequalities by exploiting the extra variables available in the robust formulations. We show that these inequalities define facets under certain conditions and that they completely describe a projection of the robust cutset polyhedron if the cutset consists of a single edge. For realistic networks and live traffic measurements, we compare the formulations and report on the speed‐up achieved by the valid inequalities. We study the “price of robustness” and evaluate the approach by analyzing the real network load. The results show that the robust optimization approach has the potential to support network planners better than present methods. © 2013 Wiley Periodicals, Inc. NETWORKS, 2013

Traffic uncertainty in toll motorway concessions in Spain

This study assessed the inaccuracy of the traffic estimates for toll motorway concessions in Spain. It was found that the estimates conducted by both the government and the concessionaire showed a significant bias towards overestimating traffic. The level of overestimation in Spain is even greater than that reported by other studies based on worldwide data. The notorious levels of overestimation entail severe burdens to the economics of the concessionaires that often prompt renegotiations of the contracts, which are often accepted by the government. These renegotiations usually end up with toll changes or extension of the concession terms, which have to be ultimately borne by future motorway users. It is postulated herein that the bias towards overestimating traffic in toll motorways in Spain is mostly caused by strategic issues rather than by modelling errors.

Paranoid Secondary: Waterfilling in a Cognitive Interference Channel with Partial Knowledge

We study a two-user cognitive channel, where the primary flow is sporadic, cannot be re-designed and operating below its link capacity. To study the impact of primary traffic uncertainty, we propose a block activity model that captures the random on-off periods of primary's transmissions. Each block in the model can be split into parallel Gaussian-mixture channels, such that each channel resembles a multiple user channel (MAC) from the point of view of the secondary user. The secondary senses the current state of the primary at the start of each block. We show that the optimal power transmitted depends on the sensed state and the optimal power profile is paranoid, i.e. either growing or decaying in power as a function of time. We show that such a scheme achieves capacity when there is no noise in the sensing. The optimal transmission for the secondary performs rate splitting and follows a layered water-filling power allocation for each parallel channel to achieve capacity. The secondary rate approaches a genie-aided scheme for large block-lengths. Additionally, if the fraction of time primary uses the channel tends to one, the paranoid scheme and the genie-aided upper bound get arbitrarily close to a no-sensing scheme.

Modeling Air-Traffic Service Time Uncertainties for Queuing Network Analysis

Numerous factors influence the operational performance of the National Airspace System (NAS). In particular, the traffic efficiency is affected by uncertainties such as weather, navigation accuracy, aircraft performance and operational procedures, and air traffic management (ATM) actions. This study focuses on identifying various air traffic uncertainty sources and deriving the associated mathematical models of service time distributions. These models provide the distributions given air traffic uncertainties through analytical expressions without resorting to computationally expensive Monte-Carlo simulations.

A real options framework to evaluate investments in toll road projects delivered under the two‐phase development strategy

PurposeThis paper aims to present a financial valuation framework based on the real options theory to evaluate investments in toll road projects delivered under the two‐phase development plan.Design/methodology/approachThe approach is based on applying the real options theory to evaluate investments in toll road projects. In particular, the risk‐neutral valuation method is used for pricing flexibility embedded in the two‐phase development plan. Risk‐neutral binomial lattice is used to model traffic uncertainty and to find the optimal time for the toll road expansion. Probabilistic life cycle cost and revenue analysis is conducted to characterize the investor's financial risk profile and determine the flexibility value of the expansion option.FindingsThe flexible, two‐phase development plan can improve the investor's financial risk profile in the toll road project through limiting the downside risk of overinvestment (i.e. decreasing the probability of investment loss) and increasing the expected investment value in a highway project.Social implicationsPrivate and public sectors can benefit from this valuation framework and use tax dollars and users' fees effectively through avoiding overinvestment in toll road projects.Originality/valueThe framework consists of several integrated features, which distinguish it from existing investment valuation models. The risk‐neutral valuation method for pricing flexibility embedded in the two‐phase development plan is applied. This real options framework is capable of characterizing traffic boundary, at which it is optimal for the investor to expand the toll road. Further, this framework provides the likelihood distribution of when the investor may expand the toll road.

A survey of WDM network reconfiguration: Strategies and triggering methods

We survey the state-of-the-art technologies in Wavelength Division Multiplexing (WDM) network reconfiguration. Our focus is the strategies and triggering methods. We compare the proposed technologies in the literature from different perspectives: traffic-changes triggered vs. other events triggered, centrally-managed strategies vs. distributed strategies, new lightpath demands known vs. future user traffic unknown, adaptive/reactive approaches vs. proactive approaches, traffic prediction based vs. traffic uncertainty proof approaches. We outline the entire landscape of the strategies and triggering methods of WDM network reconfiguration from WDM network operators’ point of view. There are gaps in research and in field trials, which must be addressed, so that the great potentials of WDM network reconfiguration may be realized to add significant flexibility in network operations.