Routing wavelength and time-slot reassignment algorithms for TDM based optical WDM networks

In this paper, we consider the problem of maximizing the time of first lightpath request rejection, T in the circuit-switched time division multiplexed (TDM) wavelength-routed (WR) optical WDM networks. TDM is incorporated into WDM, to increase the channel utilization when the carried traffic does not require the entire channel bandwidth. In TDM–WDM network, multiple sessions are multiplexed on each wavelength by assigning a sub-set of the TDM slots to each session. Thus, given a session request with a specified bandwidth, a lightpath has to be established by using the routing, wavelength and time-slot assignment (RWTA) algorithms. If the lightpath cannot be established, lightpath request rejection or call blocking occurs. As each lightpath is substantial revenue and long-lived, lightpath request rejection is highly unfavourable in the optical backbone networks. In this paper, we are proposing an intelligent routing, wavelength and time-slot reassignment algorithm for multi-rate traffic demands, where, when a call gets blocked, the already established calls in the network are rerouted, wavelength and time-slot reassigned so as to accommodate the blocked call. Since we are talking of slow arrivals and long holding times for the lightpaths, it is possible to do this reassignment while provisioning a new call. Simulation based analyses are used to study the performance of the proposed reassignment algorithm. The results show that the proposed reassignment algorithm can be used to maximize the time of first call blocking, thereby accommodating more calls in the network before upgrading the network capacity.     

Network Capacity Reliability Analysis Considering Traffic Regulation after a Major Disaster

The focuses of this paper are optimal traffic regulation after a major disaster and evaluation of capacity reliability of a network. The paper firstly discusses the context of traffic regulation and its importance after a major disaster. Then, this problem is formulated as an optimisation program in which the traffic regulator attempts to regulate the amount of traffic movements or access to some areas so as to maximise the traffic volumes in the network while (a) link flows must be less than link capacities and (b) re-routing effect due to changes of traffic condition in the network is allowed. The re-routing behaviour is assumed to follow Probit Stochastic User's Equilibrium (SUE). The paper explains an optimisation algorithm based on an implicit programming approach for solving this problem with the SUE condition. With this optimisation problem, the randomness of the link capacities (to represent random effects of the disaster) is introduced and the paper describes an approach to approximate the capacity reliability of the network using Monte-Carlo simulation. The paper then adopts this approach to evaluate the performances of different traffic regulation policies with a small network and a test network of Kobe city in Japan.     

Comparison of Methods for Path Flow Reassignment for Dynamic User Equilibrium

Models to describe or predict of time-varying traffic flows and travel times on road networks are usually referred to as dynamic traffic assignment (DTA) models or dynamic user equilibrium (DUE) models. The most common form of algorithms for DUE consists of iterating between two components namely dynamic network loading (DNL) and path inflow reassignment or route choice. The DNL components in these algorithms have been investigated in many papers but in comparison the path inflow reassignment component has been relatively neglected. In view of that, we investigate various methods for path inflow reassignment that have been used in the literature. We compare them numerically by embedding them in a DUE algorithm and applying the algorithm to solve DUE problems for various simple network scenarios. We find that the choice of inflow reassignment method makes a huge difference to the speed of convergence of the algorithms and, in particular, find that ??travel time responsive?? reassignment methods converge much faster than the other methods. We also investigate how speed of convergence is affected by the extent of congestion on the network, by higher demand or lower capacity. There appears to be much scope for further improving path inflow reassignment methods.     

分布式整数规划及其在航线扰动问题的应用

在航空公司的运作中时常会出现干扰它正常运作的现象。在这种情况下,航空公司必须马上制定航线修复计划使受到干扰的航线尽快复原,以防止更大面积的航班取消和航班延误。提出一种基于递增映射迭代方法的分布式整数规划算法来解决由于机场关闭引起的航线扰动问题。整个问题分成了两个子问题：可行航线的生成和飞机的重指派。第一个子问题的问题空间被初始点分割方法分割成了若干片段。然后在一个分布式的计算网络中使用递增映射迭代方法在分得的每个片段上同时求解第一个子问题。得到的可行航线用来求解第二个子问题。最后的算例结果可以发现提出的方法要好于CPLEX和多目标基因算法。     

Neural approximation of open-loop feedback rate control in satellite networks

A resource allocation problem for a satellite network is considered, where variations of fading conditions are added to those of traffic load. Since the capacity of the system is finite and divided in finite discrete portions, the resource allocation problem reveals to be a discrete stochastic programming one, which is typically NP-Hard. In practice, a good approximation of the optimal solution could be obtained through the adoption of a closed-form expression of the performance measure in steady-state conditions. Once we have summarized the drawbacks of such optimization strategy, we address two novel optimization approaches. The first one derives from Gokbayrak and Cassandras and is based on the minimization over the discrete constraint set using an estimate of the gradient, obtained through a "relaxed continuous extension" of the performance measure. The computation of the gradient estimation is based on infinitesimal perturbation analysis (IPA). Neither closed forms of the performance measures, nor additional feedbacks concerning the state of the system and very mild assumptions about the stochastic environment are requested. The second one is the main contribution of the present work, and is based on an open-loop feedback control (OLFC) strategy, aimed at providing optimal reallocation strategies as functions of the state of the network. The optimization approach leads us to a functional optimization problem, and we investigate the adoption of a neural network-based technique, in order to approximate its solution. As is shown in the simulation results, we obtain near-optimal reallocation strategies with a small real time computational effort and avoid the suboptimal transient periods introduced by the IPA gradient descent algorithm.     

Energy Aware Routing Algorithm in Manet Using Linear Programming

Mobile ad hoc networks (MANET) are wireless network without infrastructure and suffering from low power battery. Therefore the main objective in finding a route for traffic transfer from a given source to a given destination is to minimize the node energy consumption. This paper solves the problem of finding a route satisfying the main objective of minimum energy consumption and other QoS requirements such as minimum delay and maximum packet delivery ratio by using linear programming technique. Two cases are considered: 1. The traffic amount of a given request is transmitted into single path, and 2. The traffic amount of a request can be distributed into parallel paths. A preprocessing step is done first for network topology design. This step leads to formulate the first case as integer linear programming problem and the second case as linear programming and not mixed integer linear programming. The two obtained solutions are evaluated in terms of three criteria: energy consumption, execution time, and packet delivery ratio using an experimental study. The results show that the solution of second case is much better than the first case in terms of energy consumption and execution time. Packet delivery ratio in the second case is 100% while in the first case is only 76%.     

适于估计OD矩阵的交通检测点的最优分布

讨论了适于估计起迄点出行分布矩阵(OD矩阵)的交通检测点的合理分布问题.根 据检测点应当满足的规则,建立了关于检测点分布的非线性规划模型.在已知极点问转移概 率的前提下,将检测点的分布问题描述成一个平均报酬Markov决策过程,并通过转化为一 个等价的整数线性规划问题来求解.最后实例结果表明该模型是有效的、合理的.     

Robust network planning in nonuniform traffic scenarios

Robustness to the environmental variations is an important feature of any reliable communication network. This paper reports on a network theory approach to the design of such networks where the environmental changes are traffic fluctuations, topology modifications, and changes in the source of external traffic. Motivated by the definition of betweenness centrality in network science, we introduce the notion of traffic-aware betweenness (TAB) for data networks, where usually an explicit (or implicit) traffic matrix governs the distribution of external traffic into the network. We use the average normalized traffic-aware betweenness, which is referred to as traffic-aware network criticality (TANC), as our main metric to quantify the robustness of a network. We show that TANC is directly related to some important network performance metrics, such as average network utilization and average network cost. We prove that TANC is a linear function of end-to-end effective resistances of the graph. As a result, TANC is a convex function of link weights and can be minimized using convex optimization techniques. We use semi-definite programming method to study the properties of the optimization problem and derive useful results to be employed for robust network planning purposes.     

Evaluating the Vulnerability of Network Traffic Using Joint Security and Routing Analysis

Joint analysis of security and routing protocols in wireless networks reveals vulnerabilities of secure network traffic that remain undetected when security and routing protocols are analyzed independently. We formulate a class of continuous metrics to evaluate the vulnerability of network traffic as a function of security and routing protocols used in wireless networks. We develop two complementary vulnerability definitions using set theoretic and circuit theoretic interpretations of the security of network traffic, allowing a network analyst or an adversary to determine weaknesses in the secure network. We formalize node capture attacks using the vulnerability metric as a nonlinear integer programming minimization problem and propose the GNAVE algorithm, a Greedy Node capture Approximation using Vulnerability Evaluation. We discuss the availability of security parameters to the adversary and show that unknown parameters can be estimated using probabilistic analysis. We demonstrate vulnerability evaluation using the proposed metrics and node capture attacks using the GNAVE algorithm through detailed examples and simulation.     

Model-based traffic control

Urban traffic control is a difficult problem because of the complex interdependence of control decisions. Known techniques for achieving global control are based on parameter optimization techniques or heuristic expert systems. Optimization fails in severely congested traffic situations which require a change in global strategy. Heuristic expert systems require knowledge of all possible traffic situations, which is difficult to obtain, especially when construction and incidents cause frequent changes to the traffic network.In this paper, we show how the techniques of model-based diagnosis can be used to select coordinated control plans for networked systems of this kind Suitable local control strategies are those whose underlying assumptions are consistent with other control strategies, the state of the road network, and traffic flow. We describe a system which uses an assumption-based truth maintenance system (ATMS) to compute suitable strategies. The system has been tested both on synthetic examples and on simulations using actual data, and results are encouraging.     

Fine two-phase routing over shortest paths with traffic matrix

This paper presents an IP finely-distributed load-balanced routing scheme based on two-phase routing over shortest paths, where the traffic matrix is given. It is called the fine two-phase routing (F-TPR) scheme. F-TPR more finely distributes traffic from a source node to intermediate nodes than the original TPR. F-TPR determines the distribution ratios to intermediate nodes for each source–destination node pair independently. To determine an optimum set of distribution ratios, a linear programming (LP) formulation is derived. We compare the F-TPR scheme against the TPR scheme and the sophisticated traffic engineering (TE) scheme of Multi-Protocol Label Switching (MPLS-TE). Numerical results show that F-TPR greatly reduces the network congestion ratio compared to TPR. In addition, F-TPR provides almost the same network congestion ratios as MPLS-TE, the difference is surprisingly less than 0.1% for the various network topologies examined. In addition, considering the practical implementation of F-TPR for routers, we also investigate the case that traffic from a source node to a destination node is not allowed to be split over multiple routes. The non-split problem is formulated as an integer linear programming (ILP) problem. As it is difficult to solve the ILP problem within practical time, two heuristic algorithms are presented: Largest Traffic Demand First (LTDF) and a Random Selection (RS). The applicability of LTDF and RS are presented in terms of network size. We find that non-split F-TPR also matches the routing performance of MPLS-TE within an error of 1%, when network size is large enough.     

Load-balanced mesh router migration for wireless mesh networks

Load-balancing among domains in a wireless mesh network (WMN) is normally achieved by changing the Internet attachment of mesh routers (MRs) that carry the traffic from mobile stations (MSs). The greediness of load-balancing algorithms may force MRs to frequently change their Internet attachments, and thus degrade network performance due to inter-domain mobility of the associated MSs. In this paper, we discuss the negative impact on the performance of MSs’ mobility, due to inter-domain reassignment of MR. A MR migration scheme is proposed to achieve a tradeoff between load-balancing and inter-domain reassignment of MR. The proposed load-balancing scheme for WMNs includes: an initialization procedure to divide a WMN into domains, and a load adjustment procedure to rebalance the traffic load among the neighboring domains when required. We also provide a framework for handling inter-domain mobility in support of multi-hop communication using the Multi-hop cellular IP. Our simulation results show that the proposed protocol effectively controls MR’s change in connectivity as well as MS’s mobility.     

基于遗传算法的城市混合型路网优化设计研究

城市路网设计问题就是研究如何用定量的方法在已有交通网络上添加或扩容某些路段的问题。本文提出一种基于遗传算法的城市混合型路网设计的双层优化模型,可求出最优的用于道路网新建或改善的交通建设投资决策方案,并利用一个算例进行仿真试验,结果表明,该模型和算法是可行的,可为城市路网设计提供借鉴。     

Vehicle routing under time-dependent travel times: The impact of congestion avoidance

Daily traffic congestion forms a major problem for businesses such as logistic service providers and distribution firms. It causes late arrivals at customers and additional costs for hiring the truck drivers. Such costs caused by traffic congestion can be reduced by taking into account and avoiding predictable traffic congestion within vehicle route plans. In the literature, various strategies are proposed to avoid traffic congestion, such as selecting alternative routes, changing the customer visit sequences, and changing the vehicle-customer assignments. We investigate the impact of these and other strategies in off-line vehicle routing on the performance of vehicle route plans in reality. For this purpose, we develop a set of vehicle routing problem instances on real road networks, and a speed model that reflects the key elements of peak hour traffic congestion. The instances are solved for different levels of congestion avoidance using a modified Dijkstra algorithm and a restricted dynamic programming heuristic. Computational experiments show that 99% of late arrivals at customers can be eliminated if traffic congestion is accounted for off-line. On top of that, about 87% of the extra duty time caused by traffic congestion can be eliminated by clever congestion avoidance strategies.     

Traffic grooming, routing, and wavelength assignment in WDM transport networks with sparse grooming resources

While a single fiber strand in wavelength division multiplexing (WDM) has over a terabit-per-second bandwidth and a wavelength channel has over a gigabit-per-second transmission speed, the network may still be required to support traffic requests at rates that are lower than the full wavelength capacity. To avoid assigning an entire lightpath to a small request, many researchers have looked at adding traffic grooming to the routing and wavelength assignment (RWA) problem. In this work, we consider the RWA problem with traffic grooming (GRWA) for mesh networks under static and dynamic lightpath connection requests. The GRWA problem is NP-Complete since it is a generalization of the RWA problem which is known to be NP-Complete. We propose an integer linear programming (ILP) model that accurately depicts the GRWA problem. Because it is very hard to find a solution for large networks using ILP, we solve the GRWA problem by proposing two novel heuristics. The strength of the proposed heuristics stems from their simplicity, efficiency, and applicability to large-scale networks. Our simulation results demonstrate that deploying traffic grooming resources on the edge of optical networks is more cost effective and results in a similar blocking performance to that obtained when distributing the grooming resources throughout the optical network domain.     

Network flow models for the local access network expansion problem

In the local access network expansion problem we need to expand a given network (with tree topology) due to traffic demand increase. We can expand the network either by increasing the capacity of its edges and/or by installing equipments that concentrate the traffic of several demand points. This problem has received some attention in the literature. Here, we view the problem as an extension of the well-known capacitated minimum spanning tree problem. We adapt two types of flow-based formulations, aggregated and disaggregated formulations and present some valid inequalities to improve the linear programming bounds of the previous formulations.     

Performance analysis of an adaptive,energy-efficient MAC protocol for wireless sensor networks

We propose an adaptive and energy-efficient TDMA-based MAC protocol that significantly reduces energy consumption in the network, while efficiently handling network traffic load variations and optimizing channel utilization through a timeslot stealing mechanism and a timeslot reassignment procedure. We have analytically derived the average delay performance of our MAC protocol, with and without the timeslot stealing mechanism. Our delay model, validated via simulations, shows that the timeslot stealing mechanism can substantially improve the protocol throughput in scenarios with varying and asymmetric traffic patterns. Evaluation results show that the timeslot reassignment procedure is efficient in handling the longer timescale changes in the traffic load, while the timeslot stealing mechanism is better in handling the shorter timescale changes in the traffic patterns.     

Spare capacity provisioning for quasi-static traffic

Resource provisioning has for long been an important area of research in network design. The traffic grooming problem in optical networks is a design problem of aggregating sub-wavelength traffic demands onto lightpaths and lightpaths onto fiber links such that the required electronic switching capability, hence network cost, can be minimized. Because of the reconfiguration cost in optical grooming networks, a reactive resource provisioning approach may become inefficient, and result in revenue loss. In this paper, we propose an over-provisioning scheme, which pre-allocates the spare capacity of lightpaths to dynamic sub-wavelength traffic demands such that the network can be more agile in responding to traffic increment requests. For the single-link case, we formulate the problem as a non-linear programming problem, and for under reasonable assumptions, we prove the objective function is convex. We provide an exact algorithm to find the optimal solution. The problem with general topologies is then studied. We prove the NP-hardness in this case, and propose heuristics. Numerical results show our heuristics perform well.     

The use of learning algorithms in telephone traffic routing—A methodology

A theoretical as well as conceptual framework for the use of learning algorithms in telephone traffic routing is given. The approach is distinctly different from the mathematical programming methods generally used in such cases. Learning algorithms at the network nodes update their strategies for routing traffic on the basis of success or failure in completing calls. The entire system is described as a Markov process and different learning schemes are shown to lead to different flow patterns in the steady state.     

面向互动式网络场景再现的流速控制系统与方法

互动式网络场景再现是一种重要的真实网络流量产生方法.然而,由于流量产生过程的复杂性,基于精确的数学模型对该过程产生流速进行控制是一个较为困难的新问题.文中设计实现了一个面向互动式网络场景再现的流速控制系统,并将网络场景再现过程中的流速控制问题转化为目标跟踪控制问题进行求解.该系统采用一种基于函数近似器的流速控制方法,利用函数近似器对系统的输入输出关系进行描述,通过动态调整系统输入流量来对回放过程输出流量进行跟踪.最后,利用真实网络流量实验考察了文中系统和方法在不同丢包、传输延迟以及会话阻断环境下的实际控制效果,并从收敛时间、产生输入输出、控制误差等角度对系统的控制性能进行了分析.