Showing papers on "Network planning and design published in 2012"

Twenty-Five Years of Hub Location Research

Abstract: Last year was the 25th anniversary of two seminal transportation hub location publications, which appeared in 1986 in Transportation Science and Geographical Analysis. Though there are related hub location and network design articles that predate these works, the 1986 publications provided a key impetus for the growth of hub location as a distinct research area. This paper is not intended as a comprehensive review of hub location literature; rather, our goal is to reflect on the origins of hub location research, especially in transportation, and provide some commentary on the present status of the field. We provide insight into early motivations for analyzing hub location problems and describe linkages to problems in location analysis and network design. We also highlight some of the most recent research, discuss some shortcomings of hub location research and suggest promising directions for future effort.

Minimizing ISP network energy cost: formulation and solutions

Abstract: According to several studies, the power consumption of the Internet accounts for up to 10% of the worldwide energy consumption and is constantly increasing. The global consciousness on this problem has also grown, and several initiatives are being put into place to reduce the power consumption of the ICT sector in general. In this paper, we face the problem of minimizing power consumption for Internet service provider (ISP) networks. In particular, we propose and assess strategies to concentrate network traffic on a minimal subset of network resources. Given a telecommunication infrastructure, our aim is to turn off network nodes and links while still guaranteeing full connectivity and maximum link utilization constraints. We first derive a simple and complete formulation, which results into an NP-hard problem that can be solved only for trivial cases. We then derive more complex formulations that can scale up to middle-sized networks. Finally, we provide efficient heuristics that can be used for large networks. We test the effectiveness of our algorithms on both real and synthetic topologies, considering the daily fluctuations of Internet traffic and different classes of users. Results show that the power savings can be significant, e.g., larger than 35%.

Analysis of Cell Load Coupling for LTE Network Planning and Optimization

Abstract: System-centric modeling and analysis are of key significance in planning and optimizing cellular networks. In this paper, we provide a mathematical analysis of performance modeling for LTE networks. The system model characterizes the coupling relation between the cell load factors, taking into account non-uniform traffic demand and interference between the cells with arbitrary network topology. Solving the model enables a network-wide performance evaluation in resource consumption. We develop and prove both sufficient and necessary conditions for the feasibility of the load-coupling system, and provide results related to computational aspects for numerically approaching the solution. The theoretical findings are accompanied with experimental results to instructively illustrate the application in optimizing LTE network configuration.

Multimodal hub location and hub network design

Abstract: Through observations from real life hub networks, we introduce the multimodal hub location and hub network design problem. We approach the hub location problem from a network design perspective. In addition to the location and allocation decisions, we also study the decision on how the hub networks with different possible transportation modes must be designed. In this multimodal hub location and hub network design problem, we jointly consider transportation costs and travel times, which are studied separately in most hub location problems presented in the literature. We allow different transportation modes between hubs and different types of service time promises between origin–destination pairs while designing the hub network in the multimodal problem. We first propose a linear mixed integer programming model for this problem and then derive variants of the problem that might arise in certain applications. The models are enhanced via a set of effective valid inequalities and an efficient heuristic is developed. Computational analyses are presented on the various instances from the Turkish network and CAB data set.

QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks

Abstract: The primary aim of this paper is to present a new content-based, non-intrusive quality of experience (QoE) prediction model for low bitrate and resolution (QCIF) H.264 encoded videos and to illustrate its application in video quality adaptation over Universal Mobile Telecommunication Systems (UMTS) networks. The success of video applications over UMTS networks very much depends on meeting the QoE requirements of users. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet such QoE requirements. Video quality is affected by distortions caused both by the encoder and the UMTS access network. The impact of these distortions is content dependent, but this feature is not widely used in non-intrusive video quality prediction models. In the new model, we chose four key parameters that can impact video quality and hence the QoE-content type, sender bitrate, block error rate and mean burst length. The video quality was predicted in terms of the mean opinion score (MOS). Subjective quality tests were carried out to develop and evaluate the model. The performance of the model was evaluated with unseen dataset with good prediction accuracy ( ~ 93%). The model also performed well with the LIVE database which was recently made available to the research community. We illustrate the application of the new model in a novel QoE-driven adaptation scheme at the pre-encoding stage in a UMTS network. Simulation results in NS2 demonstrate the effectiveness of the proposed adaptation scheme, especially at the UMTS access network which is a bottleneck. An advantage of the model is that it is light weight (and so it can be implemented for real-time monitoring), and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization in network planning and content provisioning for network/service providers.

Mobility impact in IEEE 802.11p infrastructureless vehicular networks

Abstract: Vehicular ad hoc networks (VANETs) are an extreme case of mobile ad hoc networks (MANETs). High speed and frequent network topology changes are the main characteristics of vehicular networks. These characteristics lead to special issues and challenges in the network design, especially at the medium access control (MAC) layer. In this paper, we provide a comprehensive evaluation of mobility impact on the IEEE 802.11p MAC performance. The study evaluates basic performance metrics such as packet delivery ratio, throughput, and delay. An unfairness problem due to the relative speed is identified for both broadcast and unicast scenarios. We propose two dynamic contention window mechanisms to alleviate network performance degradation due to high mobility. The first scheme provides dynamic level of service priority via adaptation to the number of neighboring nodes, while the second scheme provides service priority based on node relative speed. Extensive simulation results demonstrate a significant impact of mobility on the IEEE 802.11p MAC performance, the unfairness problem in the vehicle-to-vehicle (V2V) communications, and the effectiveness of the proposed MAC schemes.

Analysis and modelling of YouTube traffic

Abstract: YouTube currently accounts for a significant percentage of the Internet's global traffic. Hence, understanding the characteristics of the YouTube traffic generation pattern can provide a significant advantage in predicting user video quality and in enhancing network design. In this paper, we present a characterisation of the traffic generated by YouTube when accessed from a regular PC. On the basis of this characterisation, a YouTube server traffic generation model is proposed, which, for example, can be easily implemented in simulation tools. The derived characterisation and model are based on experimental evaluations of traffic generated by the application layer of YouTube servers. A YouTube server commences the download with an initial burst and later throttles down the generation rate. If the available bandwidth is reduced (e.g. in the presence of network congestion), the server behaves as if the data excess that cannot be transmitted because of the reduced bandwidth were accumulated at a server's buffer, which is later drained if the bandwidth availability is recovered. As we will show, the video clip encoding rate plays a relevant role in determining the traffic generation rate, and therefore, a cumulative density function for the most viewed video clips will be presented. The proposed traffic generation model was implemented in a YouTube emulation server, and the generated synthetic traffic traces were compared with downloads from the original YouTube server. The results show that the relative error between downloads from the emulation server and the original server does not exceed 6% for the 90% of the considered videos. Copyright © 2012 John Wiley & Sons, Ltd.

NSDMiner: Automated discovery of Network Service Dependencies

Abstract: Enterprise networks today host a wide variety of network services, which often depend on each other to provide and support network-based services and applications. Understanding such dependencies is essential for maintaining the well-being of an enterprise network and its applications, particularly in the presence of network attacks and failures. In a typical enterprise network, which is complex and dynamic in configuration, it is non-trivial to identify all these services and their dependencies. Several techniques have been developed to learn such dependencies automatically. However, they are either too complex to fine tune or cluttered with false positives and/or false negatives. In this paper, we propose a suite of novel techniques and develop a new tool named NSDMiner (which stands for Mining for Network Service Dependencies) to automatically discover the dependencies between network services from passively collected network traffic. NSDMiner is non-intrusive; it does not require any modification of existing software, or injection of network packets. More importantly, NSDMiner achieves higher accuracy than previous network-based approaches. Our experimental evaluation, which uses network traffic collected from our campus network, shows that NSDMiner outperforms the two best existing solutions significantly.

A branch and cut algorithm for the container shipping network design problem

Abstract: The network design problem in liner shipping is of increasing importance in a strongly competitive market where potential cost reductions can influence market share and profits significantly. In this paper the network design and fleet assignment problems are combined into a mixed integer linear programming model minimizing the overall cost. To better reflect the real-life situation we take into account the cost of transhipment, a heterogeneous fleet, route dependent capacities, and butterfly routes. To the best of our knowledge it is the first time an exact solution method to the problem considers transhipment cost. The problem is solved with branch-and-cut using clover and transhipment inequalities. Computational results are reported for instances with up to 15 ports.

Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination

Abstract: The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms.

Transit route network design-maximizing direct and transfer demand density

Abstract: Transit network design is an important part of urban transportation planning. The purpose of this paper is to build on direct traveler density model and extend it to design transit network considering demand density relating to direct demands and transfers, and lengths of routes. The proposed method aiming to maximize demand density of route under some resource constraints divides transit network design problem into three stages, i.e., skeleton route design, main route design and branch route design, based on the objective functions with different transfer coefficients. An ant colony optimization (ACO) is used to solve the model. The model and algorithm are illustrated with data from Dalian city, China and results show that the approach can improve the solution quality if the transfer coefficient is reasonably set.

On the Overhead of Interference Alignment: Training, Feedback, and Cooperation

Abstract: Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel's maximum number of degrees of freedom. Realizing IA gains, however, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In this paper, we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired through training and analog feedback. We design the training and feedback system to maximize IA's effective sum-rate: a non-asymptotic performance metric that accounts for estimation error, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with overhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback channel quality. A main insight from our analysis is that, by properly designing the CSI acquisition process, IA can provide good sum-rate performance in a very wide range of fading scenarios. Another observation from our work is that such overhead-aware analysis can help solve a number of practical network design problems. To demonstrate the concept of overhead-aware network design, we consider the example problem of finding the optimal number of cooperative IA users based on signal power and mobility.

On the Overhead of Interference Alignment: Training, Feedback, and Cooperation

Abstract: Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel's maximum number of degrees of freedom. Realizing IA gains, however, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In this paper, we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired through training and analog feedback. We design the training and feedback system to maximize IA's effective sum-rate: a non-asymptotic performance metric that accounts for estimation error, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with overhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback channel quality. A main insight from our analysis is that, by properly designing the CSI acquisition process, IA can provide good sum-rate performance in a very wide range of fading scenarios. Another observation from our work is that such overhead-aware analysis can help solve a number of practical network design problems. To demonstrate the concept of overhead-aware network design, we consider the example problem of finding the optimal number of cooperative IA users based on signal power and mobility.

Architectures and Protocols for Capacity Efficient, Highly Dynamic and Highly Resilient Core Networks [Invited]

Abstract: The Core Optical Networks (CORONET) program addresses the development of architectures, protocols, and network control and management to support the future advanced requirements of both commercial and government networks, with a focus on highly dynamic and highly resilient multi-terabit core networks. CORONET encompasses a global network supporting a combination of IP and wavelength services. Satisfying the aggressive requirements of the program required a comprehensive approach addressing connection setup, restoration, quality of service, network design, and nodal architecture. This paper addresses the major innovations developed in Phase 1 of the program by the team led by Telcordia and AT&T. The ultimate goal is to transfer the technology to commercial and government networks for deployment in the next few years.

On-chip networks from a networking perspective: congestion and scalability in many-core interconnects

Abstract: In this paper, we present network-on-chip (NoC) design and contrast it to traditional network design, highlighting similarities and differences between the two. As an initial case study, we examine network congestion in bufferless NoCs. We show that congestion manifests itself differently in a NoC than in traditional networks. Network congestion reduces system throughput in congested workloads for smaller NoCs (16 and 64 nodes), and limits the scalability of larger bufferless NoCs (256 to 4096 nodes) even when traffic has locality (e.g., when an application's required data is mapped nearby to its core in the network). We propose a new source throttling-based congestion control mechanism with application-level awareness that reduces network congestion to improve system performance. Our mechanism improves system performance by up to 28% (15% on average in congested workloads) in smaller NoCs, achieves linear throughput scaling in NoCs up to 4096 cores (attaining similar performance scalability to a NoC with large buffers), and reduces power consumption by up to 20%. Thus, we show an effective application of a network-level concept, congestion control, to a class of networks -- bufferless on-chip networks -- that has not been studied before by the networking community.

Understanding the characteristics of cellular data traffic

Abstract: Because of rapidly growing subscriber populations, advances in cellular communication technology, increasingly capable user terminals, and the expanding range of mobile applications, cellular networks have experienced a significant increase in data traffic, the dominant part of which is carried by the http protocol. Understanding the characteristics of this traffic is important for network design, traffic modeling, resource planning and network control. In this study we present a comprehensive characterization study of mobile http-based traffic using packet level traces collected in a large cellular network. We analyze the traffic using metrics at packet level, flow level and session level. For each metric, we conduct a comparison between traffic from different applications, as well as comparison to traffic in a wired network. Finally, we discuss the implications of our findings for better resource utilization in cellular infrastructures.

Scheduled service network design with synchronization and transshipment constraints for intermodal container transportation networks

Abstract: In this paper we address the problem of scheduled service network design for container freight distribution along rivers, canals, and coastlines. We propose a new concise continuous- time mixed-integer linear programming model that accurately evaluates the time of occurrence of transportation events and the number of containers transshipped between vehicles. Given the transportation network, the eet of available vehicles, the demand and the supply of containers, the sailing time of vehicles, and the structure of costs, the objective of the model is to build a minimum cost service network design and container distribution plan that denes services, their departure and arrival times, as well as vehicle and container routing. The model is solved with a commercial solver and is tested on data instances inspired from real-world problems encountered by EU carrier companies. The results of the computational study show that in scheduled service networks direct routes happen more often when either the eet capacity is tight or the handling costs and the lead time interval increase. The increase of the same parameters leads to the decrease of the number of containers transshipped between vehicles.

Energy-aware topology design for wireless body area networks

Abstract: Wireless Body Area Networks (WBANs) represent one of the most promising approaches for improving the quality of life, allowing remote patient monitoring and other healthcare applications. In such networks, traffic routing plays an important role together with the positioning of relay nodes, which collect the information from biosensors and send it towards the sinks. This work investigates the optimal design of wireless body area networks by studying the joint data routing and relay positioning problem in a WBAN, in order to increase the network lifetime. To this end, we propose an integer linear programming model which optimizes the number and location of relays to be deployed and the data routing towards the sinks, minimizing both the network installation cost and the energy consumed by wireless sensors and relays. We solve the proposed model in realistic WBAN scenarios, and discuss the effect of different parameters on the characteristics of the planned networks. Numerical results demonstrate that our model can design energy-efficient and cost-effective wireless body area networks in a very short computing time, thus representing an interesting framework for the WBAN planning problem.

Bridge Seismic Retrofit Program Planning to Maximize Postearthquake Transportation Network Capacity

Abstract: The bridge network, as part of the critical civil infrastructure, is susceptible to natural and man-made hazards. It is essential that the network retains its traffic-carrying capacity after a disastrous earthquake to ensure efficient evacuation of at-risk population to safe zones and timely dispatch of emergency response resources to the impacted area. Because of limited resources, it is important to prioritize bridge retrofit projects and manage disaster mitigation resources under a strategic budget plan. This paper proposes a methodology to find the optimal bridge retrofit program that aims to maximize the postdisaster network evacuation capacity. The uncertainties of earthquake intensity, bridge structural damage, and bridge traffic-carrying capacities are addressed by using a Monte Carlo simulation framework with established bridge fragility curves and damage-functionality relationships, and the effectiveness of preserving evacuation capacity is calculated on the basis of a network design model. The proposed methodological framework is demonstrated with the transportation network in Memphis, Tennessee, and numerical experiments show that the proposed framework solves the problem efficiently. The modeling framework can help transportation agencies maximize the effectiveness of investment. Emergency managers can also use the model to enhance preparedness and emergency response efficiency, which in turn improves the infrastructure systems’ resilience against extreme events.

Weighted Sum-Rate Maximization in Wireless Networks: A Review

Abstract: The weighted sum-rate maximization (WSRMax) problem plays a central role in many network control and optimization methods, such as power control, link scheduling, cross-layer control, network utility maximization. The problem is NP-hard in general. In Weighted Sum-Rate Maximization in Wireless Networks: A Review, a cohesive discussion of the existing solution methods associated with the WSRMax problem, including global, fast local, as well as decentralized methods is presented. In addition, general optimization approaches, such as branch and bound methods, complementary geometric programming, and decomposition methods, are discussed in depth to address the problem. Through a number of numerical examples, the applicability of the resulting algorithms in various application domains is demonstrated. The presented algorithms and the associated numerical results can be very useful for network engineers or researchers with an interest in network design.

On the Design of Energy-Efficient Mixed-Line-Rate (MLR) Optical Networks

Abstract: With increasing energy consumption of the Internet, it is now imperative to design energy-efficient network architectures and protocols. Optical technologies have significant promise in improving the energy efficiency of network infrastructures. Future optical backbone networks will be heterogeneous in nature where a single link may carry various line-rate signals. This mixed-line-rate (MLR) network architecture is shown to be cost effective in satisfying heterogeneous traffic demands. In this study, we present mathematical models that can act as references for designing energy- and cost-efficient MLR optical networks. We also perform a comparative study of the energy efficiency of MLR and special cases of MLR design, named as single-line-rate networks (where all the links have same line rates). We explore the scenarios where the MLR networks minimize energy consumption. Finally, we investigate the relationship between energy-minimized and cost-minimized MLR network design.

Coverage prediction and optimization algorithms for indoor environments

Abstract: A heuristic algorithm is developed for the prediction of indoor coverage. Measurements on one floor of an office building are performed to investigate propagation characteristics and validations with very limited additional tuning are performed on another floor of the same building and in three other buildings. The prediction method relies on the free-space loss model for every environment, this way intending to reduce the dependency of the model on the environment upon which the model is based, as is the case with many other models. The applicability of the algorithm to a wireless testbed network with fixed WiFi 802.11b/g nodes is discussed based on a site survey. The prediction algorithm can easily be implemented in network planning algorithms, as will be illustrated with a network reduction and a network optimization algorithm. We aim to provide an physically intuitive, yet accurate prediction of the path loss for different building types.

On Reliable Broadcast in Low Duty-Cycle Wireless Sensor Networks

Abstract: Broadcast is one of the most fundamental services in wireless sensor networks (WSNs). It facilitates sensor nodes to propagate messages across the whole network, serving a wide range of higher level operations and thus being critical to the overall network design. A distinct feature of WSNs is that many nodes alternate between active and dormant states, so as to conserve energy and extend the network lifetime. Unfortunately, the impact of such cycles has been largely ignored in existing broadcast implementations that adopt the common assumption of all nodes being active all over the time. In this paper, we revisit the broadcast problem with active/dormant cycles. We show strong evidence that conventional broadcast approaches will suffer from severe performance degradation, and, under low duty cycles, they could easily fail to cover the whole network in an acceptable time frame. To this end, we remodel the broadcast problem in this new context, seeking a balance between efficiency and latency with coverage guarantees. We demonstrate that this problem can be translated into a graph equivalence, and develop a centralized optimal solution. It provides a valuable benchmark for assessing diverse duty-cycle-aware broadcast strategies. We then extend it to an efficient and scalable distributed implementation, which relies on local information and operations only, with built-in loss compensation mechanisms. The performance of our solution is evaluated under diverse network configurations. The results suggest that our distributed solution is close to the lower bounds of both time and forwarding costs, and it well resists to the wireless loss with good scalability on the network size and density. In addition, it enables flexible control toward the quality of broadcast coverage.

Integrated operations planning and revenue management for rail freight transportation

Abstract: In the rail industry, profit maximization relies heavily on the integration of logistics activities with an improved management of revenues. The operational policies chosen by the carrier have an important impact on the network yield and thus on global profitability. This paper bridges the gap between railroad operations planning and revenue management. We propose a new bilevel mathematical formulation which encompasses pricing decisions and network planning policies such as car blocking and routing as well as train make-up and scheduling. An exact solution approach based on a mixed integer formulation adapted to the problem structure is presented, and computational results are reported on randomly generated instances.