Network Capacity Planning Research Articles

Augmenting the cities’ and metropolitan regional demands for mega rail infrastructure: the application of SWOT and factor analysis

PurposeIn successfully meeting city and metropolitan growth, sustainable development is compulsory. Sustainability is a must-focus for any project, particularly for large and mega rail infrastructure. This paper aims to investigate to what degree social, environmental and economic factors influence the government when planning sustainable rail infrastructure projects. To respond to such a matter, this paper focuses on two Australian mega-rail projects: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE).Design/methodology/approachAs the basis of an experimental evaluation framework strengths, weaknesses, opportunities and threats (SWOT) and factor analysis were used. These two methods were specifically selected as comparative tools for SWRL and SWRL projects, to measure their overall sustainability effect.FindingsUsing factor analysis, in the MRE, the factors of network capacity, accessibility, employment and urban planning were seen frequently throughout the case study. However, politics and economic growth had lower frequencies throughout this case study. This difference between the high-weighted factors is likely a key element that determined the SWRL to be more sustainable than the MRE. The SWOT analysis showed the strengths the MRE had over the SWRL such as resource use and waste management, and natural habitat preservation. These two analyses have shown that overall, calculating the sustainability levels of a project can be subjective, based on the conditions surrounding various analysis techniques.Originality/valueThis paper first introduces SWRL and MRE projects followed by a discussion about their overall sustainable development. Both projects go beyond the traditional megaprojects' goal of improving economic growth by developing and enhancing infrastructure. Globally, for such projects, sustainability measures are now considered alongside the goal of economic growth. Second, SWOT and factor analysis are undertaken to further evaluate the complexity of such projects. This includes their overall sustainable development vision alignment with environmental, economic and social factors.

Supervised algorithms for application bandwidth prediction on Amazon Web Service from a rural SME

This article presents a methodology to measure the bandwidth behaviour by making predictions of the network traffic that connects to the cloud in small and medium enterprises in rural areas with difficult access in Colombia, in order to optimize network resources over time and ensure the quality of service in web applications. A comparative study of three neural network algorithms that model a multilayer neural network is performed, selecting the one that has a minimum error that approaches zero; the selected algorithm is trained from a data source to predict the network traffic that connects to the cloud.It is necessary to analyse network behaviour to ensure the quality of web applications in the cloud that transmit information such as data, images, sound, video, etc., some in real time, and that generate large volumes of traffic. Understanding the traffic flowing through the network enables network capacity planning when managing limited resources, such as in the case of small and medium-sized enterprises in rural areas. As a product of the research analysis, a free software prototype will be developed to perform the measurements and predictions in rural areas. The results of the implementation show that the proposed approach is superior to other forecasting methods in terms of accuracy and predictability.

Optimization method of distribution network energy storage and capacity planning considering uncertainty of new energy sources

The large-scale connection of the renewable energy with random and fluctuating output of the grid has a serious impact on the safe and stable operation of the grid. Considering the high cost of energy storage and the fluctuation of load, in this study, an optimization approach for designing the distribution network’s energy storage capacity is presented. This paper analyzes the uncertainty of new energy, and constructs a single distribution network energy storage station model based on the analysis results. In this paper, the typical daily total network loss is taken as the objective function for site selection and capacity planning. In this paper, particle swarm optimization algorithm is used to optimize the energy storage and capacity planning of distribution network. The experimental results show that this method can reduce the operating cost of distribution network and restrain the system load fluctuation. This method optimizes the economy and reliability of distribution network system.

ACM: Accuracy-Aware Collaborative Monitoring for Software-Defined Network-Wide Measurement.

Software-defined measurement (SDM) is a simple and efficient way to deploy measurement tasks and collect measurement data. With SDM, it is convenient for operators to implement fine-grained network-wide measurements at the flow level, from which many important functions can benefit. The prior work provides mechanisms to distribute flows to monitors, such that each monitor can identify its non-overlapped subset of flows to measure, and a certain global performance criterion is optimized, such as load balance or flow coverage. Many applications of network management can benefit from a function that can find large flows efficiently, such as congestion control by dynamically scheduling large flows, caching of forwarding table entries, and network capacity planning. However, the current network-wide measurements neglect the diversity of different flows as they treat large flows and small flows equally. In this paper, we present a mechanism of accuracy-aware collaborative monitoring (ACM) to improve the measurement accuracies of large flows in network-wide measurements at the flow level. The structure of the sketch is an approximate counting algorithm, and a high-measurement accuracy can be achieved by merging the results from multiple monitors with sketches, which is termed as collaborative monitoring. The core idea of our method is to allocate more monitors to large flows and achieve the load balance to provide accuracy-aware monitoring. We modeled our problem as an integer–linear programming problem, which is NP-hard. Thus, we propose an approximation algorithm, named the improved longest processing time algorithm (iLPTA); we proved that its approximation ratio is . We propose a two-stage online distribution algorithm (TODA). Moreover, we proved that its approximation ratio is . The iLPTA is an offline approximation algorithm used to assign monitors for each flow, which prove the validity and feasibility of the core idea. The TODA is an online algorithm that attempts to achieve the load balance by selecting the monitor with the smallest load to a large flow. Our extensional experiment results verify the effectiveness of our proposed algorithms.

On the benefits of point-to-multipoint coherent optics for multilayer capacity planning in ring networks with varying traffic profiles

Point-to-multipoint (P2MP) coherent optics using digital subcarrier multiplexing have recently been proposed as a promising new technology to reduce the cost and complexity of optical transport networks, particularly those in metro aggregation scenarios with hub-and-spoke (H&S) traffic patterns. In this paper, we perform a case study on fault tolerant ring networks with more general traffic profiles to assess the benefits of P2MP optics for alternative network scenarios such as those appearing in metro rings. We propose mixed integer linear programming formulations for multilayer capacity planning to minimize total transceiver costs, considering both point-to-point and P2MP transceivers, as well as a heuristic approach for the more complex P2MP case. Results indicate that P2MP optics can bring several advantages for all tested traffic profiles, ranging from pure uniform flows to mostly H&S. Transceiver cost savings are achieved in various cases, greater at higher network loads and for traffic patterns that are mostly H&S. Results also show that P2MP optics prompt a multilayer redesign of the network with significantly reduced spectrum usage and a reduced number of IP hops, resulting in lower optical–electronic–optical and IP processing latency and lower IP layer equipment costs for all traffic profiles.

On the Benefits of Point-to-Multipoint Coherent Optics for Multilayer Capacity Planning in Ring Networks with Varying Traffic Profiles

On the Benefits of Point-to-Multipoint Coherent Optics for Multilayer Capacity Planning in Ring Networks with Varying Traffic Profiles

Predictive Capacity Planning for Mobile Networks—ML Supported Prediction of Network Performance and User Experience Evolution

Network performance prediction is crucial for enabling agile capacity planning in mobile networks. One of the key problems is predicting evolution of spectral efficiency in growing network load conditions. The main factor driving network performance and spectral efficiency is reportedly the Channel Quality Indicator (CQI). In this paper, the performance of different Machine Learning (ML) models were examined, and XGBoost was selected as the best performing model. Furthermore, to improve modeling accuracy, several features were introduced (operating frequency band, Physical Resource Block (PRB) utilization in surrounding cells, number of surrounding cells within a radius, heavy data factor and higher order modulation usage). The impact of these features on CQI prediction were examined.

Predicting short‐term mobile Internet traffic from Internet activity using recurrent neural networks

SummaryMobile network traffic prediction is an important input into network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non‐linear patterns, or other important correlations in time series mobile network data. We compare the performance of two deep learning (DL) architectures, long short‐term memory (LSTM) and gated recurrent unit (GRU), and two conventional machine learning (ML) architectures—Random Forest and Decision Tree—for predicting mobile Internet traffic using 2 months of Telecom Italia data for Milan. K‐Means clustering was used a priori to group cells based on Internet activity, and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error and mean absolute error. Both DL algorithms were effective in modeling Internet activity and seasonality, both within days and across 2 months. We find variations in performance across clusters within the city. Overall, the DL models outperformed the conventional ML models, and the LSTM outperformed the GRU in our experiments.

Planning Fog networks for time-critical IoT requests

The massive growth of the Internet of Things (IoT) applications and the challenges of Cloud computing have increased the importance of Fog networks for timely processing the requests from delay-sensitive applications. A Fog network provides local aggregation, analysis, and processing of IoT requests that may or may not be time-critical. One of the major issues of Fog is its capacity planning considering the traffic load of time-critical requests. The response time can be huge if a time-critical request is processed on Cloud. The response time of a time-critical request can be big on the Fog layer if it is not prioritized. Hence, there is a need to handle the time-critical traffic on a priority basis at the Fog layer. In this paper, a priority queuing model with preemption has been proposed considering the mixed types of requests at the Fog layer. The proposed approach determines the required number of Fog nodes in order to satisfy the desired Quality of Service (QoS) requirements of IoT requests. The proposed mechanism is evaluated through simulations using the iFogSim simulator. The work can be used in the capacity planning of Fog networks.

Minimum battery capacity planning in wireless rechargeable sensor networks

This paper studies the problem of the minimum battery capacity required for the normal operation of each sensor when the charging path of the mobile charger is determined. When the parameters of the wireless rechargeable sensor network are determined, the aim is to minimize the battery capacity required by each sensor, and to ensure the permanent operation of the wireless rechargeable sensor network with the minimum sensor energy consumption. By analyzing the actual working conditions of mobile charger, a linear programming model with the goal of minimizing the battery capacity of each sensor is established, Lingo is used to solve the model, and the results are verified by computer simulation. The results show that the computer simulation results are basically consistent with the model solution results, which proved the reliability of the model.

Proposed Base-Station Location Optimization with Genetic Algorithm Scheme for Lte Network Radio Planning

Long-term evolution (LTE) is used widely in inefficient network technologies which serve billions of users. These technologies have the following features like higher bandwidth, high spectrum efficiency and less latency. This paper proposes minimizing the cost of a cellular network, which usually includes the optimum selection and base-station locations that meet certain capacity and coverage constraints. In the LTE wireless network framework, coverage and capacity planning are interrelated in terms of interference. Moreover, the ever-increasing capacity demand for non-uniformly distributed users makes base station location optimization an important action. This paper adopts one type of improvement, which is a genetic algorithm-based methodology that optimally performs the task of base-stations location by minimizing the cost of the network while fulfilling the coverage and capacity criteria planning by using ATDI ICS TELECOM. Reducing 19.95% of the overlap of the area will result in good coverage of the proposed GA thus, improving efficiency of the network.

Inter-Datacenter Bulk Transfers: Trends and Challenges

Many modern Internet services and applications operate on top of geographically distributed datacenters, which replicate large volumes of business data and other content, to improve performance and reliability. This leads to high volumes of continuous network traffic between datacenters, constituting the bulk of traffic exchanged over the Wide-Area Networks (WANs) that connect datacenters. Operators heavily rely on the efficient and timely delivery of such traffic, as it is key for the performance of distributed datacenter applications, and bulk inter-datacenter traffic also plays an important role for network capacity planning. In this article, we discuss the unique and salient features of bulk across-datacenter transfers and extensively review recent technologies and existing research solutions proposed in the literature for optimization of such traffic. Moreover, we discuss several challenges and interesting directions that call for substantial future research efforts.

FAVE: A Fast and Efficient Network Flow AVailability Estimation Method With Bounded Relative Error

Capacity planning and sales projection are essential tasks for network operators. This work aims to help network providers to carry out network capacity planning and sales projection by answering: Given topology and capacity, whether the network can serve current flow demands with high probabilities? We name such probability as the “ flow availability ”, and present the flow availability estimation (FAVE) problem with generalizing the classical network connectivity based and maximum flow based reliability estimations. To quickly estimate flow availabilities, we utilize correlations among link and flow failures to figure out the importance of roles played by different links in flow failures (i.e., flow demands could not be satisfied). And we design three sequential importance sampling (SIS) estimation methods, which are: (1) Accurate and efficient : They achieve a bounded or even vanishing relative error with linear computational complexities. Hence they can provide more accurate estimations in less simulation time. (2) Robust and scalable: They maintain such estimation efficiencies even if only a partial SEED set information is available, or when the FAVE problem is extended to the multiple flows case. When applying to a realistic backbone network, our method can reduce the flow availability estimation cost by 900 and 130 times compared with MC and baseline IS methods; and also facilitate capacity planning and sales projection by providing better flow availability guarantees, compared with traditional methods.

Performance Comparison of QoS Deployment Strategies for Cellular Network Services

Differentiated quality-of-Service (QoS) techniques are widely used to distinguish between different service classes and prioritize service needs in mobile networks. Mobile Network Operators (MNOs) utilize QoS techniques to develop strategies that are supported by the mobile network infrastructure. However, QoS deployment strategy can ensure that the radio resources provided by the base station are easily consumed if it is not used correctly or when different techniques are used all together. In this paper, we propose a scheduling algorithm and compare two different QoS deployment strategies for prioritized User Equipment (UEs) (with higher scheduling rates and dedicated bandwidth) that MNOs can use in the current infrastructure, using a commercial real-time Long Term Evolution (LTE) network in different test scenarios. Moreover, we expose the real-time user experience in terms of uplink throughput and analyze results of the UE’s real-time key performance indicators (KPIs) in detail. Experiment results are evaluated considering the implications of different QoS support types on network coverage and capacity planning optimizations. Our results demonstrate that even though pre-configured resource allocations can be given to prioritize UEs, the experience of all the UEs can be affected unexpectedly in the presence of many UEs who have received different QoS deployment support. Our experimental observations have revealed that location of the UEs with respect to Base Station (BS) and the availability of dedicated bandwidth UEs inside cell may have implications on the apriori defined resource allocation strategies of the other UEs.

Analysis of a Semi-Open Queuing Network with a State Dependent Marked Markovian Arrival Process, Customers Retrials and Impatience

We consider a queuing network with single-server nodes and heterogeneous customers. The number of customers, which can obtain service simultaneously, is restricted. Customers that cannot be admitted to the network upon arrival make repeated attempts to obtain service. The service time at the nodes is exponentially distributed. After service completion at a node, the serviced customer can transit to another node or leave the network forever. The main features of the model are the mutual dependence of processes of customer arrivals and retrials and the impatience and non-persistence of customers. Dynamics of the network are described by a multidimensional Markov chain with infinite state space, state inhomogeneous behavior and special structure of the infinitesimal generator. The explicit form of the generator is derived. An effective algorithm for computing the stationary distribution of this chain is recommended. The expressions for computation of the key performance measures of the network are given. Numerical results illustrating the importance of the account of the mentioned features of the model are presented. The model can be useful for capacity planning, performance evaluation and optimization of various wireless telecommunication networks, transportation and manufacturing systems.

Capacity Planning of LoRa Networks With Joint Noise-Limited and Interference-Limited Coverage Considerations

LoRa networks are emerging low power wide area networks (LP-WAN) that are configured as star-of-star networks. Within each underlying star network, potentially thousands of LoRa end-devices utilize the patented chirp spread spectrum-based LoRa modulation to communicate directly with a LoRa gateway over long distances at the scale of kilometers. Typically, each LoRa gateway supports six quasi-orthogonal logical star networks that correspond to the available LoRa modulation spreading factors. The aggregate capacity of a LoRa gateway is evaluated as the number of end-devices that could be supported within the corresponding cell area by all six available spreading factor networks while fulfilling some QoS coverage requirement. Successful coverage of LoRa signals could be mainly attributed to noise-limited and interference-limited considerations. In the LoRa literature, these two factors have been treated in an isolated manner, where independent SNR and SIR thresholds have been set to signify coverage. However, in reality, the SNR threshold for successful coverage depicts a decreasing function of the SIR experienced by the end-device of interest. In this paper, the capacity of LoRa networks is evaluated with joint noise and interference considerations. The presented framework for capacity evaluation has the advantage that it derives the cumulative capacity distribution that could be supported from the cell-edge towards the cell-center of an individual LoRa gateway. It is then demonstrated that the capacity distribution curves could be employed in the LoRa network planning of an example IoT home security application.

Anomaly-Tolerant Network Traffic Estimation via Noise-Immune Temporal Matrix Completion Model

Accurately estimating origin-destination (OD) network traffic is crucial for network management and capacity planning. However, the potential network anomaly and complex noise make this goal difficult to achieve. Existing network traffic estimation methods usually impute network traffic independent of anomaly detection, which ignores the potential relationship between the two tasks to help each other in achieving better performance. Moreover, these approaches can only be suitable for simple Gaussian or outlier noise assumptions, which cannot be applied to more complex noise distributions in practical applications. To address these issues, we propose a novel anomaly-tolerant network traffic estimation approach for simultaneously estimating network traffic and detecting network anomaly. Specifically, by utilizing the inherent low-rank property and temporal characteristic of traffic matrix, we formulate the network traffic estimation problem as a noise-immune temporal matrix completion (NiTMC) model, where the complex noise is fitted by mixture of Gaussian (MoG), and the network anomaly is smoothed by the $L_{2,1}$ -norm regularization. In addition, we also design a convergence-guaranteed optimization algorithm based on the expectation maximization (EM) and block coordinate update (BCU) methods to solve the proposed model. Furthermore, to deal with large-scale network problems, we develop a scalable and memory-efficient algorithm by employing stochastic proximal gradient descent (SPGD) method. Finally, the extensive experiments performed on real datasets demonstrate that our proposed NiTMC model outperforms the previously widely used network traffic estimation methods.

Performance Analysis of One Model of Communication and Information System in Military Operation

This paper presents a model of communication and information system in military operations. Here OPNET MODELER simulation package is applied because it is suitable for network modelling, topology and capacity planning. Simulation of different types of IP traffic and monitor their performance to optimise the functionality of network elements, management performance network applications, and as well as in research and development of new network technologies. Application of the method of mass service are determined by the capacity needed for voice transmission on the links in the model and using the OPNET MODELER simulation program are analysed performance modeled communication information system in data transmission. The results of the simulation are presented through target the service settings: workload links communication and information system, e-mail download response time, http page download response time and packet loss in data transfer. The aim of the research has shown that modeled communication information system with defined elements (nodes), the capacity of links (according to the specification of telecommunication devices) and defined traffic can respond to the requirements of command forces in the military operation in terms of telecommunication service. The results of the analysed service target parameters show that modeled communication and information system provides an efficient flow of information and the tra nsfer of voice and IP data for the needs of command and control in military operations.

Spatial Cluster Detection in Mobility Networks: A Copula Approach

Summary In mobility network capacity planning, characterizing the mobility network traffic is one of the most challenging tasks. Besides the growth trend and multiple periodic temporal patterns for normal traffic, the problem is complicated by the occasionally intense traffic for special events and its dynamic spatial relationships. Identifying the areas that have different traffic patterns compared with their neighbouring areas is a problem of spatial hotspot detection. In the paper, a copula-based method is proposed: using a multivariate extreme value copula, the upper tail dependence of the traffic distributions of neighbouring cell towers is evaluated, and then a cluster of multiple time series (i.e. multiple cell towers) with high upper tail dependence is detected. The method proposed is validated by using synthetic data as well as real mobility traffic data.

On the synthetic dataset generation for IPTV services based on user behavior

The adoption of the paradigm shift from push-based media broadcasting to pull-based media streaming has seen a significant growth in the recent decade. IPTV is good example to illustrate this claim. In IPTV systems hundreds (or maybe thousands in near future) of live TV channels and video contents are available to subscribers. In many application domains a clear understanding of access pattern to the items is necessary. However, for security reasons, in IPTV systems this kind of information is not publicly available. In this paper, taking into account a model which mimics the behavior of a typical IPTV user, and with the aid of MovieLens dataset, we produce a trace file or synthetic dataset, named UBSDI. We then show that, this dataset can reflect many properties of real datasets quite realistically. This dataset is publically available and can be used in many applications such as recommender systems, network capacity planning, network dimensioning, and system performance optimization.