Showing papers on "Network planning and design published in 2016"

Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach

Abstract: Understanding and forecasting mobile traffic of large scale cellular networks is extremely valuable for service providers to control and manage the explosive mobile data, such as network planning, load balancing, and data pricing mechanisms. This paper targets at extracting and modeling traffic patterns of 9,000 cellular towers deployed in a metropolitan city. To achieve this goal, we design, implement, and evaluate a time series analysis approach that is able to decompose large scale mobile traffic into regularity and randomness components. Then, we use time series prediction to forecast the traffic patterns based on the regularity components. Our study verifies the effectiveness of our utilized time series decomposition method, and shows the geographical distribution of the regularity and randomness component. Moreover, we reveal that high predictability of the regularity component can be achieved, and demonstrate that the prediction of randomness component of mobile traffic data is impossible.

Traffic Engineering in Software-Defined Networking: Measurement and Management

Abstract: As the next generation network architecture, software-defined networking (SDN) has exciting application prospects. Its core idea is to separate the forwarding layer and control layer of network system, where network operators can program packet forwarding behavior to significantly improve the innovation capability of network applications. Traffic engineering (TE) is an important network application, which studies measurement and management of network traffic, and designs reasonable routing mechanisms to guide network traffic to improve utilization of network resources, and better meet requirements of the network quality of service (QoS). Compared with the traditional networks, the SDN has many advantages to support TE due to its distinguish characteristics, such as isolation of control and forwarding, global centralized control, and programmability of network behavior. This paper focuses on the traffic engineering technology based on the SDN. First, we propose a reference framework for TE in the SDN, which consists of two parts, traffic measurement and traffic management. Traffic measurement is responsible for monitoring and analyzing real-time network traffic, as a prerequisite for traffic management. In the proposed framework, technologies related to traffic measurement include network parameters measurement, a general measurement framework, and traffic analysis and prediction; technologies related to traffic management include traffic load balancing, QoS-guarantee scheduling, energy-saving scheduling, and traffic management for the hybrid IP/SDN. Current existing technologies are discussed in detail, and our insights into future development of TE in the SDN are offered.

A green intermodal service network design problem with travel time uncertainty

Abstract: In a more and more competitive and global world, freight transports have to overcome increasingly long distances while at the same time becoming more reliable. In addition, a raising awareness of the need for environmentally friendly solutions increases the importance of transportation modes other than road. Intermodal transportation, in that regard, allows for the combination of different modes in order to exploit their individual advantages. Intermodal transportation networks offer flexible, robust and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect these advantages, it is the challenge to develop models which both represent multiple modes and their characteristics (e.g., fixed-time schedules and routes) as well as the transhipment between these transportation modes. In this paper, we introduce a Green Intermodal Service Network Design Problem with Travel Time Uncertainty (GISND-TTU) for combined offline intermodal routing decisions of multiple commodities. The proposed stochastic approach allows for the generation of robust transportation plans according to different objectives (i.e., cost, time and greenhouse gas (GHG) emissions) by considering uncertainties in travel times as well as demands with the help of the sample average approximation method. The proposed methodology is applied to a real-world network, which shows the advantages of stochasticity in achieving robust transportation plans.

Orchestrating Tree-Type VNF Forwarding Graphs in Inter-DC Elastic Optical Networks

Abstract: It is known that by incorporating network function virtualization (NFV) in inter-datacenter (inter-DC) networks, service providers can use their network resources more efficiently and adaptively and expedite the deployment of new services. This paper studies the provisioning algorithms to realize tree-type virtual network function forwarding graphs (VNF-FGs), i.e., multicast NFV trees (M-NFV-Ts), in inter-DC elastic optical networks (IDC-EONs) cost-effectively. Specifically, we try to optimize the VNF placement and multicast routing and spectrum assignment jointly for orchestrating M-NFV-Ts in an IDC-EON with the lowest cost. Our study addresses both static network planning and dynamic network provisioning. For network planning, we first formulate a mixed integer linear programming (MILP) model to solve the problem exactly, and then propose three heuristic algorithms, namely, auxiliary frequency slot matrix (AFM)-MILP, AFM-GS, and RB. Extensive simulations show that AFM-MILP and AFM-GS can approximate the MILP's performance on low-cost M-NFV-T provisioning with much shorter running time. For network provisioning, we design two additional online algorithms based on AFM-GS and RB to serve M-NFV-Ts in a dynamic IDC-EON, with the consideration of spectrum fragmentation.

Critical Links and Nonlocal Rerouting in Complex Supply Networks.

Abstract: Link failures repeatedly induce large-scale outages in power grids and other supply networks. Yet, it is still not well understood which links are particularly prone to inducing such outages. Here we analyze how the nature and location of each link impact the network's capability to maintain a stable supply. We propose two criteria to identify critical links on the basis of the topology and the load distribution of the network prior to link failure. They are determined via a link's redundant capacity and a renormalized linear response theory we derive. These criteria outperform the critical link prediction based on local measures such as loads. The results not only further our understanding of the physics of supply networks in general. As both criteria are available before any outage from the state of normal operation, they may also help real-time monitoring of grid operation, employing countermeasures and support network planning and design.

Optimized LTE Cell Planning With Varying Spatial and Temporal User Densities

Abstract: Base station (BS) deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for fourth-generation (4G) cellular networks using metaheuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterward, we implement a metaheuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently proposed gray-wolf optimizer) to find suboptimal BS locations that satisfy both problem constraints in the area of interest, which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant BSs. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regard to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality-of-service (QoS) targets are always reached, even for large-scale problems.

Energy-Efficient Virtual Base Station Formation in Optical-Access-Enabled Cloud-RAN

Abstract: In recent years, the increasing traffic demand in radio access networks (RANs) has led to considerable growth in the number of base stations (BSs), posing a serious scalability issue, including the energy consumption of BSs. Optical-access-enabled Cloud-RAN (CRAN) has been recently proposed as a next-generation access network. In CRAN, the digital unit (DU) of a conventional cell site is separated from the radio unit (RU) and moved to the “cloud” (DU cloud) for centralized signal processing and management. Each DU/RU pair exchanges bandwidth-intensive digitized baseband signals through an optical access network (fronthaul). Time-wavelength division multiplexing (TWDM) passive optical network (PON) is a promising fronthaul solution due to its low energy consumption and high capacity. In this paper, we propose and leverage the concept of a virtual base station (VBS), which is dynamically formed for each cell by assigning virtualized network resources, i.e., a virtualized fronthaul link connecting the DU and RU, and virtualized functional entities performing baseband processing in DU cloud. We formulate and solve the VBS formation (VF) optimization problem using an integer linear program (ILP). We propose novel energy-saving schemes exploiting VF for both the network planning stage and traffic engineering stage. Extensive simulations show that CRAN with our proposed VF schemes achieves significant energy savings compared to traditional RAN and CRAN without VF.

Service Network Design with Resource Constraints

Abstract: We first present a new service network design model for freight consolidation carriers, one that selects services and routes both commodities and resources needed to support the services that transport them, while explicitly recognizing that there are limits on how many resources are available at each terminal. We next present a solution approach that combines column generation, meta-heuristic, and exact optimization techniques to produce high-quality solutions. We demonstrate the efficacy of the approach with an extensive computational study and benchmark its performance against both a leading commercial solver and a column generation-based heuristic.

Capacitated transit service network design with boundedly rational agents

Abstract: This paper proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. Within a time-discretized space-time network, the time-dependent transit services are characterized by traveling arcs and waiting arcs with constant travel times. Instead of using traditional flow-based formulations, an agent-based integer linear formulation is proposed to represent boundedly rational decisions under strictly imposed capacity constraints, due to vehicle carrying capacity and station storage capacity. Focusing on a viable and limited sets of space-time path alternatives, the proposed single-level optimization model can be effectively decomposed to a time-dependent routing sub-problem for individual agents and a knapsack sub-problem for service arc selections through the Lagrangian decomposition. In addition, several practically important modeling issues are discussed, such as dynamic and personalized transit pricing, passenger inflow control as part of network restraint strategies, and penalty for early/late arrival. Finally, numerical experiments are performed to demonstrate the methodology and computational efficiency of our proposed model and algorithm.

Two-phase stochastic program for transit network design under demand uncertainty

Abstract: This paper develops a reliability-based formulation for rapid transit network design under demand uncertainty. We use the notion of service reliability to confine the stochastic demand into a bounded uncertainty set that the rapid transit network is designed to cover. To evaluate the outcome of the service reliability chosen, flexible services are introduced to carry the demand overflow that exceeds the capacity of the rapid transit network such designed. A two-phase stochastic program is formulated, in which the transit line alignments and frequencies are determined in phase 1 for a specified level of service reliability; whereas in phase 2, flexible services are determined depending on the demand realization to capture the cost of demand overflow. Then the service reliability is optimized to minimize the combined rapid transit network cost obtained in phase 1, and the flexible services cost and passenger cost obtained in phase 2. The transit line alignments and passenger flows are studied under the principles of system optimal (SO) and user equilibrium (UE). We then develop a two-phase solution algorithm that combines the gradient method and neighborhood search and apply it to a series of networks. The results demonstrate the advantages of utilizing the two-phase formulation to determine the service reliability as compared with the traditional robust formulation that pre-specifies a robustness level.

A new multi-criteria scenario-based solution approach for stochastic forward/reverse supply chain network design

Abstract: Analyzing current trends in supply chain management, lead to find unavoidable steps toward closing the loop of supply chain. In order to expect best performance of Closed-Loop Supply Chain (CLSC) network, an integrated approach in considering design and planning decision levels is necessary. Further, real markets usually contain uncertain parameters such as demands and prices of products. Therefore, the next important step is considering uncertain parameters. In order to cope with designing and planning a closed-loop supply chain, this paper proposes a multi-period, multi-product closed-loop supply chain network with stochastic demand and price in a Mixed Integer Linear Programming (MILP) structure. A multi criteria scenario based solution approach is then developed to find optimal solution through some logical scenarios and three comparing criteria. Mean, Standard Deviation (SD), and Coefficient of Variation (CV), which are the mentioned criteria for finding the optimal solution. Sensitivity analyses are also undertaken to validate efficiency of the solution approach. The computational study reveals the acceptability of proposed solution approach for the stochastic model. Finally, a real case study in an Indian manufacturer is evaluated to ensure applicability of the model and the solution methodology.

Reliable Supply Chain Network Design

Abstract: Risk management in supply chains has been receiving increased attention in the past few years. In this article, we present formulations for the strategic supply chain network design problem with dual objectives, which usually conflict with each other: minimizing cost and maximizing reliability. Quantifying the total reliability of a network design is not as straightforward as total cost calculation. We use reliability indices and develop analytical formulations that model the impact of upstream supply chain on individual entities’ reliability to quantify the total reliability of a network. The resulting multiobjective nonlinear model is solved using a novel hybrid algorithm that utilizes a genetic algorithm for network design and linear programming for network flow optimization. We demonstrate the application of our approach through illustrative examples in establishing tradeoffs between cost and reliability in network design and present managerial implications.

Network behavior data collection and analytics for anomaly detection

Abstract: In one embodiment, a method includes receiving at an analytics module operating at a network device, network traffic data collected from a plurality of sensors distributed throughout a network and installed in network components to obtain the network traffic data from packets transmitted to and from the network components and monitor network flows within the network from multiple perspectives in the network, processing the network traffic data at the analytics module, the network traffic data comprising process information, user information, and host information, and identifying at the analytics module, anomalies within the network traffic data based on dynamic modeling of network behavior. An apparatus and logic are also disclosed herein.

Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework

Abstract: . Sensors and sensor networks play an important role in decision-making related to water quality, operational streamflow forecasting, flood early warning systems, and other areas. In this paper we review a number of existing applications and analyse a variety of evaluation and design procedures for sensor networks with respect to various criteria. Most of the existing approaches focus on maximising the observability and information content of a variable of interest. From the context of hydrological modelling only a few studies use the performance of the hydrological simulation in terms of output discharge as a design criterion. In addition to the review, we propose a framework for classifying the existing design methods, and a generalised procedure for an optimal network design in the context of rainfall–runoff hydrological modelling.

An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample

Abstract: Display OmittedThis paper's Graphical abstractWhen using the RBF neural network to deal with small samples with high feature dimension and few numbers, too many inputs are difficult to determine the numbers of hidden layer neurons, it influences the design structure of the network, the redundancies or correlative data will influence the training of the network, and relatively few number of samples make network train non-completed or over-fitted, thereby affecting the operating efficiency and recognition accuracy of neural network.For the problem of small sample classification, two aspects of RBF neural network are optimized. Firstly, the original data reduces their feature dimension by PLS algorithm, then the low dimensional data is used as network input, it regard as external optimization. And then, using genetic algorithm to optimize RBF, the optimization way adopts hybrid coding and simultaneous evolving for hidden layer neurons and connection weights, this step regard as internal optimization. By these two consecutive optimizations, an optimized RBF neural network algorithm based on PLS and GA (PLS-GA-RBF algorithm) for small sample is established, which facilitates the hidden layer of network design, and improves the network training speed and generalization ability, thereby improving the operating efficiency and recognition accuracy of the network.The new algorithm is ingenious combination of the advantages of three algorithms, it realize the external optimization by PLS and internal optimization by GA. PLS-GA-RBF algorithm can fit more complex nonlinear recognition problems, and is more suitable for the small sample classification, which with high feature dimension and fewer numbers.In order to verify the reliability of the PLS-GA-RBF algorithm, multiple instances is used to validate and analysis. In this paper, four different experiments are arranged; among them are three small sample test and one large sample test. The purpose of the arrangement large sample test is to compare of validation. The result is satisfactory, which means the new algorithm has unique superiority in dealing with the small sample. The nature of small sample is well-analyzed.PLS is employed to reduce feature dimension of small sample, which obtained the relatively ideal low-dimensional data as the inputs of neural network.Unlike previous studies, the optimized GA-RBF algorithm is adopts the way of hybrid coding and simultaneous evolving for hidden layer neurons and connection weights.By two consecutive optimization, combining the advantages of three algorithms of PLS, GA, and RBF, a reliable small sample classification algorithm (PLS-GA-RBF) is established.Four different groups of experiments are arranged to valuate the classification ability of PLS-GA-RBF algorithm. Radial basis function (RBF) neural network can use linear learning algorithm to complete the work formerly handled by nonlinear learning algorithm, and maintain the high precision of the nonlinear algorithm. However, the results of RBF would be slightly unsatisfactory when dealing with small sample which has higher feature dimension and fewer numbers. Higher feature dimension will influence the design of neural network, and fewer numbers of samples will cause network training incomplete or over-fitted, both of which restrict the recognition precision of the neural network. RBF neural network has some drawbacks, for example, it is hard to determine the numbers, center and width of the hidden layer's neurons, which constrain the success of training. To solve the above problems, partial least squares (PLS) and genetic algorithm(GA)are introduced into RBF neural network, and better recognition precision will be obtained, because PLS is good at dealing with the small sample data, it can reduce feature dimension and make low-dimensional data more interpretative. In addition, GA can optimize the network architecture, the weights between hidden layer and output layer of the RBF neural network can ease non-complete network training, the way of hybrid coding and simultaneous evolving is adopted, and then an accurate algorithm is established. By these two consecutive optimizations, the RBF neural network classification algorithm based on PLS and GA (PLS-GA-RBF) is proposed, in order to solve some recognition problems caused by small sample. Four experiments and comparisons with other four algorithms are carried out to verify the superiority of the proposed algorithm, and the results indicate a good picture of the PLS-GA-RBF algorithm, the operating efficiency and recognition accuracy are improved substantially. The new small sample classification algorithm is worthy of further promotion.

A Method to Evaluate Total Supply Capability of Distribution Systems Considering Network Reconfiguration and Daily Load Curves

Abstract: The total supply capability (TSC) is an important index for assessing the reliability of a distribution power system. In this paper, two models to evaluate the TSC are established. In the first, the TSC is acquired with the conditions that all load outages can be restored via network reconfiguration with transformers' N-1 contingencies, i.e., that all constraints related to branch thermal ratings and bus-voltage limits can be satisfied following restoration for each N-1 contingency. The second model, which is revision of the first, considers the daily load curves for different classes of customers, e.g., residential, commercial and industrial. Both models can be formulated as mixed integer problems with second-order cone programming (MISOCP), which can be solved using commercially available optimization software. Two test systems are used to demonstrate the applicability of the presented models. Numerical results show that the presented model is more accurate than the previously published models. This proposed analytical approach can be applied in a range of network planning studies, e.g., for selecting appropriate ratings of transformers, or for optimal locating of circuit breakers and distributed energy resources.

Mobile Network Traffic Prediction Using MLP, MLPWD, and SVM

Abstract: Mobile networks are critical for today's social mobility and the Internet. More and more people are subscribing to mobile networks, which has led to substantial demands. The network operators need to find ways of meeting the huge demands. Since mobile network resources, such as spectrum, are expensive, there is a need for efficient management of network resources as well as finding a way to predict future use for network management and planning. Network planning is crucial for network operators to provide services that are both cost effective and have high degree of quality of service (QoS). The aim of this research is to apply data analysis techniques to support network operators to maximize the resource usage for network operators, that is, to prevent both under-provisioning and over-provisioning. Therefore, this paper investigates the prediction accuracy of machine learning techniques -- Multi-Layer Perceptron (MLP), Multi-Layer Perceptron with Weight Decay (MLPWD), and Support Vector Machines (SVM) -- using a dataset from a commercial trial mobile network. The experimental results show that SVM outperforms MLP and MLPWD in predicting the multidimensionality of the real-life network traffic data, while MLPWD has better accuracy in predicting the unidimensional data. Our experimental results can help network operators predict future demands and facilitate provisioning and placement of mobile network resources for effective resource management.

SplayNet: towards locally self-adjusting networks

Abstract: This paper initiates the study of locally self-adjusting networks: networks whose topology adapts dynamically and in a decentralized manner, to the communication pattern $\sigma$ . Our vision can be seen as a distributed generalization of the self-adjusting datastructures introduced by Sleator and Tarjan, 1985: In contrast to their splay trees which dynamically optimize the lookup costs from a single node (namely the tree root), we seek to minimize the routing cost between arbitrary communication pairs in the network. As a first step, we study distributed binary search trees (BSTs), which are attractive for their support of greedy routing. We introduce a simple model which captures the fundamental tradeoff between the benefits and costs of self-adjusting networks. We present the SplayNet algorithm and formally analyze its performance, and prove its optimality in specific case studies. We also introduce lower bound techniques based on interval cuts and edge expansion, to study the limitations of any demand-optimized network. Finally, we extend our study to multi-tree networks, and highlight an intriguing difference between classic and distributed splay trees.

Enabling Urban Logistics Services at La Poste through Multi-Echelon Location-Routing

Abstract: We present a large-scale static and deterministic mixed-integer linear programming (MILP) model solving a two-echelon capacitated location-routing problem (2E-CLRP) with modal choice in the context of urban logistics services (ULS). This model aims to support the development of profitable ULS by guiding the strategic decision making of postal operators as they design an optimal facility network and vehicle fleet for the centralized consolidation and transportation of inbound and outbound urban freight flows. After comprehensively analyzing operating data from La Poste, we identify the key determinants of an optimal infrastructure and fleet design for the centralized coordination and consolidation of urban freight flows under a global service time constraint. Further, we discuss the optimal design’s sensitivity to changes in the input data and parameters of the 2E-CLRP model. The main theoretical contributions of this work are an optimal routing cost estimation formula and an optimization heuristic. Togeth...

Second best toll pricing within the framework of bounded rationality

Abstract: The network design problem is usually formulated as a bi-level program, assuming the user equilibrium is attained in the lower level program. Given boundedly rational route choice behavior, the lower-level program is replaced with the boundedly rational user equilibria (BRUE). The network design problem with boundedly rational route choice behavior is understudied due to non-uniqueness of the BRUE. In this study, thus, we mainly focus on boundedly rational toll pricing (BR-TP) with affine link cost functions. The topological properties of the lower level BRUE set are first explored. As the BRUE solution is generally non-unique, urban planners cannot predict exactly which equilibrium flow pattern the transportation network will operate after a planning strategy is implemented. Due to the risk caused by uncertainty of people’s reaction, two extreme scenarios are considered: the traffic flow patterns with either the minimum system travel cost or the maximum, which is the “risk-prone” (BR-TP-RP) or the “risk-averse” (BR-TP-RA) scenario respectively. The upper level BR-TP is to find an optimal toll minimizing the total system travel cost, while the lower level is to find the best or the worst scenario. Accordingly BR-TP can be formulated as either a min –min or a min –max program. Solution existence is discussed based on the topological properties of the BRUE and algorithms are proposed. Two examples are accompanied to illustrate the proposed methodology.

Optimal Active Distribution Network Planning: A Review

Abstract: The active distribution network is a new solution to the flexible utilization of distributed energy resources to suit the characteristics of the distribution network. Advanced “active” network management is to coordinate “generation, network, load” optimization and achieve the right balance between operational expenditure (OPEX) and capital expenditure (CAPEX). To demonstrate the advancement from introducing an active distribution network, the key features of distribution network planning mainly including traditional models is first introduced. Extensive literature in generic planning is then summarized and categorized in terms of objective functions, system modeling, solution algorithms, and tools. This is followed by an extended review and in-depth discussion of concepts and representative topic developments in optimal active distribution network planning. In contrast to traditional planning, it takes into account the effects from a range of active network interventions that are exercised at dif...