A Survey on Client Throughput Prediction Algorithms in Wired and Wireless Networks

Network communication has become a part of everyday life, and the interconnection among devices and people will increase even more in the future. Nevertheless, prediction of Quality of Service parameters, particularly throughput, is quite a challenging task. In this survey, we provide an extensive insight into the literature on Transmission Control Protocol throughput prediction. The goal is to provide an overview of the used techniques and to elaborate on open aspects and white spots in this area. We assessed more than 35 approaches spanning from equation-based over various time smoothing to modern learning and location smoothing methods. In addition, different error functions for the evaluation of the approaches as well as publicly available recording tools and datasets are discussed. To conclude, we point out open challenges especially looking in the area of moving mobile network clients. The use of throughput prediction not only enables a more efficient use of the available bandwidth, the techniques shown in this work also result in more robust and stable communication.

Intelligent Resource Allocation for Massive Internet of Things Traffic

The Internet of Things (IoT) is considered a major trend in computing and in specific areas such as Smart Cities, Smart Grid, Industry 4.0, and mobile applications based on 5G. Typically, this set of technologies requires the orchestration of heterogeneous resources that are allocated over distinct infrastructures such as Cloud Computing, Cloud of Things, Datacenters, and network backbones. Consistent with this demand, the PSIoT-Orch framework was designed to orchestrate massive IoT traffic and to allocate network resources between Aggregators and Consumers in a Publish / Subscribe strategy. This dissertation aims to build an intelligent module for PSIoT-Orch that is capable of handling data types with different transmission requirements, aiming at the efficient use of a limited communication link. The proposed component uses Reinforcement Learning, more specifically, the SARSA algorithm to dynamically adjust the available bandwidth according to transmission priority. This solution, named PSIoT-SARSA, is validated in a simulation environment under the statistical methods of Analysis of Variance and Response Surface Analysis and, at the end of the study, it is observed that it obtained promising results. The contributions are focused on gathering an approach that allows allocating bandwidth in an intelligent way, allowing efficient scheduling of the IoT flow, in the scenario of the Smart Grid.

Bandwidth Estimation and Optimized Bitrate Selection for Dynamic Adaptive Streaming Over Http Using RSI-GM And ISSO

Dynamic Adaptive Streaming over HTTP (DASH) is an emerging solution that aims to standardize existing proprietary streaming systems. DASH specification defines the media presentation description (MPD), which describes a list of available content, URL addresses, and the segment format. High bandwidth demands in interactive streaming applications pose challenges in efficiently utilizing the available bandwidth. In this paper, a novel Relative Strength Index (RSI) with Geometric mean (GM) namely RSI-GM is proposed for estimating available bandwidth for DASH. The proposed work starts by taking the video as an input at the transmitter side and then the video compression is performed using the TRLE. Then MD5 hashing-based AES encryption is applied to the compressed video data to provide data security. Then RSI-GM is proposed to estimate the available bandwidth for DASH. Finally, after estimation, the bitrate for estimated bandwidth is selected optimally using the Improved Shark Smell Optimization (ISSO) algorithm.

Optical BPSK Modulation and Demodulation using Opti System Simulator

Fibre optics deals with study of propagation of light through transparent dielectric waveguides. The fibre optics is used for transmission of data from point to point location. Fibre optic systems currently used are most extensively as the transmission line between terrestrial hardwired systems. The carrier frequencies used in conventional systems had the limitations in handling the volume and rate of the data transmission. Greater the carrier frequency larger is the available bandwidth and information carrying capacity. This paper explains about Optical BPSK, where input data is converted to BPSK data which is optically modulated by optical modulator and transmitted through an optical fibre cable. The transmitted data and received data are compared in the end.

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

AN EFFICIENT ADAPTATION OF UHD VIDEO STREAMING OVER INTERNET

The main challenge of multimedia applications is how to transmission the Ultra-High Definition (UHD) video streaming in real time over the internet. The real time video streaming suffer from difficulties to be flexible and efficiency cause the wide variation of the available internet bandwidth. To avoid the problems that introduces with internet, in this work the HEVC with required video network adaptive streaming are proposed and tested using different six levels of three UHD video (4K, FHD, 720p, 4CIF, CIF and QCIF). From different experiments that applied find the optimal configuration of H.265 encoding features for six levels to obtain the required PSNR with range (32-38 dB). The important part in this project is a controller that worked incorporate with the encoder (H.265) to obtain the video streaming adaptation on the available bandwidth of the channel. The controller continuously reads the status of channel buffer, then choosing the proper level of video to be transmitted over the channel. The work architecture is content two parts: First, the H.265 codec that apply on the three raw videos with optimal parameters configuration to compress them and get videos with lower bit rate and acceptable quality. Second, the compressed videos, based to controller.....

HIGH QUALITY LOW BITRATE VOICE CODEC FOR TRANSMISSION OVER ADVANCED LTE

In mobile communication systems bit-rate reductions while maintaining an acceptable voice quality are necessary to achieve efficiency in channel bandwidth utilization and users satisfaction. As Long-Term Evolution(LTE) converging towards all-IP solutions and supporting VOIP service, the voice signals are converted into coded digital bit-stream and sent over the network. This paper proposes the implementation of codebook excited linear prediction (CELP) voice codec algorithm based on two source-rates of low 9.6Kbps and medium 16Kbps for achieving a perceptible level of voice quality, while efficiently using available bandwidth during the transmission over advanced LTE. The architecture of proposed CELP codec model is implemented to decompose the voice signal into a set of parameters that characterize each particular frame at the encoder part, these parameters are quantized and encoded for transmission to the decoder. The investigation showed that the configuration of the link and the applied CELP codec mode mainly influence on the obtained voice capacity and quality. The quantifying also shows that the voice quality can be traded for the enhanced capacity, since the low rate codec will produce lower voice quality than higher rate codec. Also, this paper is achieved, during theconfiguration of the system with higher channel quality indicator (CQI) index, increasing in the capacity gain to a saturated value of about 500 and 1000 users per cell over 5MHz bandwidth for transmit diversity (TD) and Open-Loop Spatial Multiplexing (OLSM) respectively and up to 1000 and 2000 users per cell over 10MHz channel bandwidth for TD and OLSM respectively.

Dissecting Cloud Gaming Performance with DECAF

Cloud gaming platforms have witnessed tremendous growth over the past two years with a number of large Internet companies including Amazon, Facebook, Google, Microsoft, and Nvidia publicly launching their own platforms. While cloud gaming platforms continue to grow, the visibility in their performance and relative comparison is lacking. This is largely due to absence of systematic measurement methodologies which can generally be applied. As such, in this paper, we implement DECAF, a methodology to systematically analyze and dissect the performance of cloud gaming platforms across different game genres and game platforms. DECAF is highly automated and requires minimum manual intervention. By applying DECAF, we measure the performance of three commercial cloud gaming platforms including Google Stadia, Amazon Luna, and Nvidia GeForceNow, and uncover a number of important findings. First, we find that processing delays in the cloud comprise majority of the total round trip delay experienced by users, accounting for as much as 73.54% of total user-perceived delay. Second, we find that video streams delivered by cloud gaming platforms are characterized by high variability of bitrate, frame rate, and resolution. Platforms struggle to consistently serve 1080p/60 frames per second streams across different game genres even when the available bandwidth is 8-20× that of platform's recommended settings. Finally, we show that game platforms exhibit performance cliffs by reacting poorly to packet losses, in some cases dramatically reducing the delivered bitrate by up to 6.6× when loss rates increase from 0.1% to 1%. Our work has important implications for cloud gaming platforms and opens the door for further research on comprehensive measurement methodologies for cloud gaming.

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

In this paper we study a mobility-aware call admission control algorithm in a mobile hotspot. To this end, a vehicle is considered which has an access point with a fixed capacity. The vehicle alternates between stop and moving phases. When the vehicle is in the stop phase, it services new and handover calls by prioritizing them via a probabilistic bandwidth reservation (BR) policy. Based on this policy, new handover calls may enter the reservation space with a predefined probability. When the vehicle is in the moving phase, it services new calls only. In that phase, two different policies are considered: (a) the classical complete sharing (CS) policy, where new calls are accepted in the system whenever there exists available bandwidth, and (b) the probabilistic BR policy. Depending on the selected policy in the moving phase, we propose the probabilistic BR loss model (if the CS policy is selected) and the generalized probabilistic BR loss model (if the probabilistic BR policy is selected). In both stop and moving phases, where the call arrival process is Poisson, calls require a single bandwidth unit in order to be accepted in the system, while the service time is exponentially distributed. To analytically determine call blocking probabilities and the system’s utilization, we propose efficient iterative algorithms based on two-dimensional Markov chains. The accuracy of the proposed algorithms is verified via simulation.

Cell on Wheels-Unmanned Aerial Vehicle System for Providing Wireless Coverage in Emergency Situations

Due to natural disasters, unmanned aerial vehicles (UAVs) can be deployed as aerial wireless base stations when conventional cellular networks are out of service. They can also supplement the mobile ground station to provide wireless devices with improved coverage and faster data rates. Cells on wheels (CoWs) can also be utilized to provide enhanced wireless coverage for short-term demands. In this paper, a single CoW cooperates with a single UAV in order to provide maximum wireless coverage to ground users. The optimization problem is formulated to find the following: (1) the optimal 2D placement of the CoW, (2) the optimal 3D placement of the UAV, (3) the optimal bandwidth allocation, (4) the percentage of the available bandwidth that must be provided to the CoW and UAV, and (5) the priority of wireless coverage; which maximizes the number of covered users. We utilize the exhaustive search (ES) and particle swarm optimization (PSO) algorithms to solve the optimization problem. The effectiveness of the proposed algorithms is validated using simulation results.