Traffic Allocation Research Articles

Traffic Offloading With Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity.

Network Resource Constrained Traffic Allocation for Delay Sensitive Mobile Crowdsourcing

In mobile opportunistic networks, a common assumption is that nodes are able to complete as much data exchange as needed during a communication opportunity, which is, however, not the case in practice due to limited wireless link bandwidth. Beyond that, the storage capacity at a node is also limited, further impacting the communication efficiency. In this paper, we explore delay sensitive and network resource constrained traffic allocation for mobile device-to-device crowdsourcing. With the requirement of restricted node storage and link bandwidth, we first formulate a non-linear traffic allocation optimization problem that would be at least as hard as NP-hard. In order to practically solve it, based on the submodular property, we propose an approximation algorithm and a distributed heuristic in the same design principle. We implement the latter on Dell Streak tablets and deploy an experiment with 21 nodes for a period of three weeks. Moreover, we extract the implementation codes from the prototype and run simulations using the Haggle trace to study its performance trend. The experiment and simulation outcomes verify that the proposed mechanisms achieve the close-to-optimal performance with affordable computation complexity, which can be easily implanted in practical network environment.

Vulnerability Assessment for Cascading Failure in the Highway Traffic System

Vulnerability assessment is of great significance to highway traffic system. As the widespread cascading failure may cause serious damage, factor identification is necessary to improve the security level of highway system. In this paper, the Dematel method is applied to identify the vulnerability priorities of important factors, and a new assessment method for highway system vulnerability is presented by considering system cascading failure. The traffic allocation and reallocation are then optimized under user equilibrium assignment model in different scenarios. Finally, the methodology is applied to the Heilongjiang highway network and the results shows that vulnerable sections in highway networks can be determined efficiently in our proposed process.

A stochastic programming approach for Shelter location and evacuation planning

Shelter location and traffic allocation decisions are critical for an efficient evacuation plan. In this study, we propose a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to nearest shelters and to shortest paths within a tolerance degree to minimize the expected total evacuation time. Our model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for a potential earthquake in Istanbul. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values.

Balancing flow table occupancy and link utilization in software-defined networks

Software-Defined Networking (SDN) employs a centralized control with a global network view and provides great opportunities to improve network performance. However, due to the limitation of flow-table space at the switches and unbalanced traffic allocation on links, an SDN may suffer from flow-table overflow and inefficient bandwidth allocation among flows, increasing the controller’s burden and degrading network performance. In this paper, we present a dynamic routing scheme named DIFF that differentiates flows based on their impact on network resource and adaptively selects routing paths for them to mitigate the problems of flow-table overflow and inefficient bandwidth allocation. DIFF pre-generates a set of paths for each pair of source–destination edge switches and intelligently selects the paths from the pre-generated path-sets for new flows with an objective to balance flow-table utilizations. It adaptively reroutes some elephant flows to achieve maximum throughput under the rule of max–min fair bandwidth allocation. Simulation results show that DIFF simultaneously balances the flow-table and link utilizations, reduces the controller’s workload and packet delay, while increasing network throughput, compared with baseline schemes.

A turn based traffic allocation game

In this paper, we consider a game in which players needing to transport goods through road A or B, make decisions in turn how much goods they transport using each road. The cost per unit goods transported on road A is constant and on road B is proportional to the total goods on that road. The objective of each player is to minimize its own cost while considering other players’ choices. We present a partial solution to the n-player case and a full solution to the two player case.

A Hybrid TDMA/CSMA-Based Wireless Sensor and Data Transmission Network for ORS Intra-Microsatellite Applications.

To achieve launch-on-demand for Operationally Responsive Space (ORS) missions, in this article, an intra-satellite wireless network (ISWN) is presented. It provides a wireless and modularized scheme for intra-spacecraft sensing and data buses. By removing the wired data bus, the commercial off-the-shelf (COTS) based wireless modular architecture will reduce both the volume and weight of the satellite platform, thus achieving rapid design and cost savings in development and launching. Based on the on-orbit data demand analysis, a hybrid time division multiple access/carrier sense multiple access (TDMA/CSMA) protocol is proposed. It includes an improved clear channel assessment (CCA) mechanism and a traffic adaptive slot allocation method. To analyze the access process, a Markov model is constructed. Then a detailed calculation is given in which the unsaturated cases are considered. Through simulations, the proposed protocol is proved to commendably satisfy the demands and performs better than existing schemes. It helps to build a full-wireless satellite instead of the current wired ones, and will contribute to provide dynamic space capabilities for ORS missions.

Adaptive Batch Coding: A Balanced Congestion Control Strategy for Multi-Beam Antenna Networks

Multi-Beam Smart Antennas (MBSAs) achieve concurrent communications in multiple beams, thereby providing higher throughput compared to regular directional antennas. Most of the transport control schemes used today are based on TCP, which is too conservative since it indiscriminately reduces the window size upon any packet loss. The other extremity is the de-congestion control strategy, which abandons both window size control and retransmission, aggressively saturates the network, and compensates packet loss via network coding. To adapt to the features of the MBSA-based networks, this paper proposes a balanced transport control strategy between the above two categories, which is based on the idea of Adaptive Batch Coding (ABC) . The proposed ABC scheme resists random loss through redundant coding and copes with congestion via window shrink and retransmission. Using a cross-layer design (between transport and routing layer), both the coding scheme and the traffic allocation are adaptively adjusted according to network conditions. A customized simulation system has been developed to comprehensively evaluate the performance of the proposed ABC protocol. Experimental results show that the proposed scheme overcomes the drawbacks of those two extremities. It achieves both high throughput and high throughput (under packet loss) in MBSA-based networks.

Sensitivity analysis of the near-road dispersion model RLINE - An evaluation at Detroit, Michigan

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies.

A Distributed ADMM Approach With Decomposition-Coordination for Mobile Data Offloading

Mobile data offloading allows alternative network systems such as Wi-Fi hotspot access points (APs) to offload the traffic that is originally targeted for the cellular network operators (CNOs). It can alleviate the cellular network congestions and enhance users' quality-of-service. One of the main challenges of the mobile data offloading is to develop simple and distributed traffic allocation strategies that can optimally allocate the cellular data traffic from multiple CNOs to the APs. In this work, we propose a distributed optimization framework with decomposition-coordination to solve this problem. In our framework, the utility maximization problem of the cellular network system with mobile data offloading is formulated as a nonsmooth convex optimization problem. We then divide this problem into a set of subproblems, and each of them is solved by a CNO or an AP using its local information. All the subproblems are coordinated with each other by a virtual data offloading coordinator (VDOC), which can collect the intermediate calculation values from CNOs and APs. The VDOC will then feedback the coordination results calculated from the collected values to the corresponding CNOs and APs. We propose two distributed algorithms that can achieve the global optimization solution under different scenarios. The first one is the multiblock proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM). In this algorithm, the communication between the VDOC and CNOs or APs is assumed to be fully synchronized, and the VDOC will only feedback the coordination result after successfully receiving all the values from APs and CNOs. We relax this assumption by introducing the second algorithm referred to as the distributed asynchronized ADMM (Async-ADMM) algorithm. In this algorithm, the coordination between the VDOC and APs or CNOs does not need to be perfectly synchronized and the VDOC will feedback a coordination result whenever it receives a value from at least one AP or CNO. We prove that both algorithm can achieve the global optimal solution. We present the numerical results to verify the performance of our proposed approaches under various network settings and conditions.

Traffic Allocation Mode of PPP Highway Project: A Risk Management Approach

Highway projects are the favorites of public‐private partnership (PPP) investors because of their stable cash flow. However, there are high uncertainties in terms of traffic volume, resulting in unpredictable revenues, which has drawn major concern of PPP investors. For a road in a network, the traffic volume is determined by the traffic allocation rate, which is affected not only by the total traffic volume in the region but also by other traffic risk factors, such as travel time, toll rates, and travelling comfort. The conventional traffic allocation forecasting technique predominantly depends on the travel time, overlooking other risk factors. Consequently, traffic allocation forecasting is usually inaccurate. To improve the accuracy of traffic allocation forecasting in PPP road projects, this paper proposes to consider the effect of traffic risks together with traffic time by using the mean utility. Multinomial logit (MNL) model based on mean utility is used to predict the traffic allocation rate. To validate the proposed model, the system dynamic (SD) modeling is established to forecast the traffic volume of a case highway using the proposed traffic allocation forecasting model. The simulated result shows that the simulated traffic volume of past years from the proposed model is highly consistent with the actual one, evidencing that the proposed model can greatly improve the accuracy of the traffic forecasting.

Traffic Allocation for Low-Latency Multi-Hop Networks With Buffers

For buffer-aided tandem networks consisting of relay nodes and multiple channels per hop, we consider two traffic allocation schemes, namely local allocation and global allocation, and investigate the end-to-end latency of a file transfer. We formulate the problem for generic multi-hop queuing systems and subsequently derive closed-form expressions of the end-to-end latency. We quantify the advantages of the global allocation scheme relative to its local allocation counterpart, and we conduct an asymptotic analysis on the performance gain when the number of channels in each hops increases to infinity. The traffic allocations and the analytical delay performance are validated through simulations. Furthermore, taking a specific two-hop network with millimeter-wave (mm-wave) as an example, we derive lower bounds on the average end-to-end latency, where Nakagami- $m$ fading is considered. Numerical results demonstrate that, compared with the local allocation scheme, the advantage of global allocation grows as the number of relay nodes increases, at the expense of higher complexity that linearly increases with the number of relay nodes. It is also demonstrated that a proper deployment of relay nodes in a linear mm-wave network plays an important role in reducing the average end-to-end latency, and the average latency decays as the mm-wave channels become more deterministic. These findings provide insights for designing multi-hop mm-wave networks with low end-to-end latency.

Collaborative Multimedia Source-Protocol Coordination: A Cross-Layer QoE Study in Modern Wireless Networks

Multiple image/video source neighbors in modern wireless systems such as device-to-device (D2D) networks can significantly improve multimedia quality of experience (QoE) in content sharing. However, the exploration of multisource diversity in multimedia communications has largely been overlooked in the past. In this paper, we propose a new scheme to improve user equipment's (UE) multimedia QoE by optimizing the source traffic dispatching and resource allocation strategies from multiple UE sources in cross-layer wireless D2D networks while assuring the latency constraint. This approach results in two major contributions. First, the proposed approach optimally controls the source server traffic for each multimedia packet with regard to distortion reduction (e.g., quality contribution) and the channel conditions. Second, optimal packet retry limits are adjusted on each direct D2D communication link to achieve effective but unequal resource allocation. Simulation results show that the cross-layer collaborative source-level traffic control and protocol-level resource optimization strategy improve D2D multimedia transmission quality.

Multi-RAT Dynamic Spectrum Access for 5G Heterogeneous Networks: The SPEED-5G Approach

Research and development of technologies that address the challenges of predicted growth in mobile connections and traffic volume is well known. A major challenge is the cost of meeting the objective, in terms of both infrastructure and deployment. Today, lack of dynamic control across wireless network resources is leading to unbalanced spectrum loads and a perceived capacity bottleneck. The solutions proposed by SPEED-5G through extended dynamic spectrum access (eDSA) address traffic allocation over heterogeneous wireless technologies, better load balancing across available spectrum bands, and capacity boosting through aggregation of available resources while ensuring fair coexistence. The objective of this article is to present a new framework for MAC and RRM layers for supporting eDSA and requirements of the next-generation networks.

Multi-lane Roundabout Capacity Evaluation

Although two-lane roundabouts theoretically exhibit excellent operating performance, in practice, safety problems arise because of inappropriate driving behavior. Turbo roundabouts, which are characterized by a much higher level of safety, are alternatives to classic two-lane roundabouts, but the capacity-related benefits derived from such roundabouts remain an open issue. Accordingly, this study uses an equilibrium traffic flow allocation approach to evaluate multi-lane roundabout capacity based on gap acceptance theory. Capacity levels are calculated and compared for different gap acceptance parameters, including local parameters, and different traffic flow scenarios. It is found that the capacity of minor approaches on turbo roundabouts is always higher than on two-lane roundabouts, but that the main approaches on two-lane roundabouts exhibit better performance in terms of fully equilibrium traffic allocation. This state, however, cannot be achieved for every demand scenario. The results depend strongly on traffic movements and gap acceptance parameters indicating the need for local calibration processes.

Traffic Calming Measures: An Evaluation of the Effect on Driving Speed

Road accidents are one of the leading causes of death in the world, particularly among young people. Excessive speed is one of the main risk factors in road traffic safety, increasing accident probability and affecting accident severity. Experimental research of the traffic calming measures allocation effect on the driving speed is presented in this paper. The research has been carried out on two aspects. The first one with respect to the mean speed and the second one regarding instantaneous speed. However, the paper is not only restricted by the above research. Standardized survey interview and questioning, a survey of public opinion, was carried out to find out the road users’ opinions about the need for traffic calming measures and speed control measures. Finally, the authors presented their insights and recommendations for the installation of speed humps and gateways and their optimum spacing.

Energy Efficiency of Cloud Virtual Machines: From Traffic Pattern and CPU Affinity Perspectives

Networking and machine virtualization play critical roles in the success of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past, including the impact from network traffic. When it comes to virtual machines (VMs) in cloud data centers, the interplay between energy consumption and network traffic, however, becomes much more complicated. Through real-world measurement on both Xen- and KVM-based platforms, we show that these state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling network transactions, generating excessive interrupt requests with ceaseless context switching, which in turn, increases energy consumption. Even when a physical machine is in an idle state, its VM's network transactions will incur nontrivial energy consumption. More interestingly, the energy consumption significantly varies with traffic allocation strategies and virtual CPU affinity conditions , which was not seen in conventional physical machines. Looking closely into the virtualization architectures, we then pinpoint the root causes and examine that our measurement results can be extended for various network configurations. Moreover, we also provide initial solutions toward optimizing energy consumption in virtualized environments.

Elastic All-Optical Networks: A New Paradigm Enabled by the Physical Layer. How to Optimize Network Performances?

A new era for optical networks has been started by the development of transceivers based on digital signal processing and operating multilevel modulation formats with coherent and equalized receivers, enabling uncompensated optical links and dynamic transparent wavelength routing in nodes. Connectivity matrices at the physical layer have changed from sparse and static to full and elastic . Therefore, to fully exploit network potentialities and to implement the elasticity paradigm requested by the forecasts of worldwide Internet traffic evolution, the orchestration of logical and physical layer has become a firm requirement. We propose the statistical network assessment process (SNAP) as a method to explore potentialities of physical layer independently of specific traffic allocation. We apply SNAP to a German and to a Pan-EU network topology, first deriving the average bit-rate per lightpath given an any-to-any connectivity matrix—a static metric—in order to compare merits of three typical fiber types and to evaluate benefits of time-division hybrid modulation formats. Then, assuming physical layer characteristics are set, we perform a SNAP investigation versus progressive network loading evaluating the blocking probability versus total allocated traffic—a dynamic metric—comparing merits of different size in traffic grooming operating together with multi- or fixed-rate transceivers. We also show how the progressive loading analysis can be used to address selected physical layer upgrades. As an example, we investigate possible upgrades to hybrid Raman/Erbium fiber amplification on selected links, then displaying quantitative benefits in terms of enabled excess traffic at a given blocking probability.

Traffic Allocation Strategies in WSS-Based Dynamic Optical Networks

Elastic optical networking (EON) is a viable solution to meet future dynamic capacity requirements of Internet service provider and inter-datacenter networks. At the core of EON, wavelength selective switches (WSSs) are applied to individually route optical circuits, while assigning an arbitrary bandwidth to each circuit. Critically, the WSS control scheme and configuration time may delay the creation time of each circuit in the network. In this paper, we first detail the WSS-based optical data-plane implementation of a metropolitan network test-bed. Then, we review a software-defined networking (SDN) application designed to enable dynamic and fast circuit setup. Subsequently, we introduce a WSS logical model that captures the WSS time-sequence and is used to estimate the circuit-setup response time. Then, we present two batch service policies that aim to reduce the circuit-setup response time by bundling multiple WSS reconfiguration steps into a single SDN command. Resulting performance gains are estimated through simulation.

HGL: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks

Satellite networks provide complete connectivity and worldwide data transmission capability for constructing the Internet of Things. However, because of the varying Internet of Things traffic density, satellite networks may endure imbalanced traffic requirements and frequent link congestion. To effectively resolve these problems and optimally transmit Internet of Things data, a novel hybrid global-local load balancing routing scheme for Low Earth Orbit satellite networks is proposed in this article. Hybrid global-local load balancing routing scheme enables satellites to route Internet of Things traffic through global planning and local real-time adjustments in two steps. In hybrid global-local load balancing routing scheme, given the predictive nature of Internet of Things traffic distribution and Low Earth Orbit satellite networks, the inter-satellite traffic demand is decomposed into a predictable long-range baseline and unpredictable short-range fluctuations. A global strategy is employed first for preliminary global traffic allocation based on long-range baselines, and a local strategy is then employed for route adjustments based on short-range fluctuations. With the combination of global planning and local real-time adjustments, network traffic can eventually obtain a near-optimal allocation. Numerical simulations indicate that in contrast to single-strategy schemes, hybrid global-local load balancing routing scheme can more thoroughly eliminate congestion, and it performs better in measures such as packet loss rate, average queuing delay, traffic distribution, route oscillation, and communication overhead.