Network Interface Research Articles

A Multi-criteria based handover algorithm for vehicle-to-infrastructure communications

Maintaining seamless quality of service (QoS) requirements for ongoing delay sensitive applications during handover processes for Vehicle-to-Infrastructure (V2I) communication over heterogeneous networks is a challenging task. The situation becomes even more challenging to deal with for multi-tier heterogeneous networks with various network sizes. To overcome the above challenges, we propose a multicriteria-based handover algorithm for V2I communications (V2I-MHA) to be used in multi-tier heterogeneous network environments. V2I-MHA selects the most appropriate underlying target network for handover based on QoS profiles of the ongoing applications and services. Moreover, our proposed algorithm filters out the inappropriate candidate networks using a multi-criteria decision-making technique based on Simple Additive Weighting (SAW) approach. However, our approach ensures that the most appropriate candidate network for handover processes is selected not only based on QoS requirements of the ongoing applications, but also on the knowledge of candidate network parameters (e.g. bandwidth, packet latency, packet losses, and service pricing). A multi-mode vehicle On-Board Unit (OBU) containing Long Term Evolution-Advanced (LTE-A) and Wi-Fi network interface cards is developed in the Riverbed (previously OPNET) simulator for system performance evaluation. The simulation results show that the proposed V2I-MHA outperforms (in terms of handover failure rate and packet losses) the existing handover methods.

FastUDP: a highly scalable user-level UDP framework in multi-core systems for fast packet I/O

Nowadays, many applications, e.g., network routers, distributed data process engines, firewall, need to transfer packets at linear rate. With the increasing data volume, the performance of cluster in data center is suffering increasingly severe congestion problem of massive message packets. Constructing a high-performance stream methodology of massive small message packets is fundamentally challenging. Although many works have been proposed to address the shortcomings, inefficiency of sending massive small packets via UDP protocol in traditional Linux kernel implementation is persisting, which includes high overhead from socket operations, suboptimal scalability in multi-core systems, nonsupport of multiple network interface card (NIC) ports. In this paper, we present FastUDP, a highly efficient and scalable user-level UDP-based network stack optimization in multi-core systems. FastUDP addresses the inefficiencies from the following three novel designs: (1) enabling the exclusive thread model for improving scalability; (2) adopting a poll mode and batched operation for increasing computing resource utilization; (3) constructing a shared hugepage memory pool to eliminate the context switch overhead. Moreover, to support high throughput, FastUDP also proposes a novel work-queue-based approach to allow concurrent packet to transfer over multiple NIC ports. Based on a 40-core machine, the evaluation shows that FastUDP represents a significant improvement in the packet transfer throughput by up to 13× and reduces the packet transfer latency by up to 4.14× compared to the latest Linux (4.4.0) UDP stack. Besides, it ameliorates the performance of realistic application (memcached) by 36 to 67% compared to those on the Linux stack.

ERFR-CTC: Exploiting Residual Frequency Resources in Physical-Level Cross-Technology Communication

In Internet of Things (IoT), physical-level cross-technology communication (CTC) enables IoT gateways to communicate with heterogeneous nodes economically. However, because of bandwidth asymmetry between heterogeneous technologies, many residual frequency resources are often not fully utilized. Without modification on hardware, in this article, we consider the coexistence of ultralow power (ULP) and WiFi nodes, and propose ERFR-CTC that enables an IoT gateway to fully exploit residual frequency resources without adding additional cost. With ERFR-CTC, the gateway can simultaneously communicate with ULP and WiFi nodes only via a single WiFi network interface card (NIC), which is not only economic but also very efficient. In particular, ERFR-CTC enables ULP nodes to correctly demodulate ULP signals without being interfered by WiFi signals. We then develop theoretical models to quantify available residual frequency resources and analyze the system throughput. Finally, extensive simulations verify that our model is very accurate and show that ERFR-CTC can increase the system throughput by up to 51.4%.

Virtual Connection: Selective Connection System for Energy-Efficient Wearable Consumer Electronics

Wearable consumer electronics such as smartwatch and smartglasses generally use peer-to-peer (P2P) communications to connect to their companion devices such as smartphones. The recent wearable consumer electronics are equipped with multiple heterogeneous network interfaces, which have different power consumption and transfer bandwidths. To optimize energy efficiency, a network interface must be selected for communication depending on workload or system state. Such a technique is referred to as selective connection. However, the current selective connection systems cannot support the P2P-based wearable devices. We propose a protocol-independent user-level system, called virtual connection, that decouples applications from network-specific operations for selective connections. The proposed virtual connection system consists of user-level API and user-level middleware components. To establish the usefulness of our virtual connection, we present a case study of the selective P2P connection for wearable consumer electronics equipped with Bluetooth and Wi-Fi Direct. We demonstrate that, compared to existing selective connection systems, the P2P-based selective connection system implemented with the virtual connection can reduce the energy consumption of the wearable consumer electronics by 1.26–2.31 times.

A Unified Interface Library Open-IES-Sim Platform

In the future, integrated functions and energy internet will become the direction of smart energy development. This puts forward requirements for power flow calculation and simulation of combined heat and power supply and unified modeling. Due to the complexity of the tespy heat network equipment model, the lack of node modeling corresponding to the power flow calculation model brings difficulties to the simulation modeling of the unified power and power flow calculation. Therefore, it is necessary to unify the interface of thermal network and power flow calculation model. Based on the tespy and pandapower libraries, this paper unifies the interface functions of heat network and power grid power flow calculation modeling, constructs a unified interface library Open-IES-Sim platform and builds a coupled power flow calculation process method and corresponding visualization results display program. The corresponding open source example is given. It is convenient for the future use of scientific research workers who need thermal and electrical coupling modeling and the demonstration research in education and teaching.

Language Choice and Identity of Karelian and Belarusian Youth (according to Polls of Ethnic Leaders)

A new perspective on the analysis of the linguistic and ethnocultural situation in the Republic of Karelia and the Republic of Belarus is offered in the study. The novelty of the study is seen in the fact that, taking into account various initial parameters — geographic distance, form of government, language status — it is proposed to compare the situation in two regions, in which a general tendency is found due to the dominant position of the Russian language. The main attention is paid to clarifying the ethnic well-being of people, to varying degrees, involved in the processes of preserving language and culture. An interpretation and comparison of the reasons for the preference of the Russian language in the network space and in real life is proposed. The article is based on the results of a survey of the translators of the Vkontakte social network interface, materials from biographical interviews with ethnic leaders, and observation data on ethnically oriented events in the regions under consideration. The difference in linguistic attitudes and real practices of using the Karelian and Belarusian languages by activists was revealed. The linguistic reasons (the level of linguistic competence) and the psychological aspect of the ethno-linguistic component in the choice of the dominant language in Karelia and Belarus have been determined.

Optimal Selection of Heterogeneous Network Interfaces for High-Speed Rail Communications

Integrating millimeter Wave (mmWave) and long-term evolution (LTE) sub-6 GHz bands to provide wireless services for high-speed rails (HSRs) is a promising solution to balance the ever-rising service demand and operational cost. In this paper, we optimize the network interface selection of the mmWave and LTE interfaces for HSR communications, to minimize the operational cost while satisfying the service requests of passengers. The network interface selection problem is formulated as a finite-horizon discrete-time Markov decision process (MDP) problem. Based on the file requests and associated deadlines of a passenger and the train schedule, we solve the MDP problem ahead-of-time by using dynamic programming (DP) and achieve optimal interface selection for each waypoint along the railways. Further, by exploiting the subadditivity and superadditivity of the considered problem, we reveal that an optimal switch of interfaces is only triggered at the moment when the remaining undelivered data or the elapsed time reaches their specific thresholds. Based on the thresholds, the optimal interface selection can be directly achieved for every waypoint, hence eliminating the need for DP and substantially reducing the computational complexity. The proposed algorithms are evaluated with practical train schedules. Simulation results show that the proposed algorithms can substantially reduce the operational cost by 35% in our considered simulation setting, as compared to existing alternatives.

Optical networking within the Lightwave Energy-Efficient Datacenter project [Invited

The Lightwave Energy-Efficient Datacenter (LEED) project within the ARPA-e ENLITENED program is developing novel energy-efficient multichannel lightwave networks. These networks are enabled by a new optical “rotor” switch that can reconfigure the network topology in less than 20 µs and a field-programmable-gate-array-based network interface controller called Corundum that can provide precise network-wide synchronization of packets admitted into the lightwave network. Here we review the optical networking research within LEED and discuss future directions.

Differences in online reviews caused by distribution channels

Since the use of social networks has become part of daily life for most people, many online travel agencies (OTAs) are allowing customers to log in using account credentials for social network services like Facebook (social network interface systems, SNIS) without having to create a local account on OTA sites themselves. This research explores the roles of SNIS in review generation on OTAs. For this purpose, we collected more than 174,000 online reviews for London hotels, posted from 2015 to 2016 on TripAdvisor.com. Through a series of analyses, we find that (1) concerning review generation, the portion of online reviews posted via SNIS shows no significant increase during the period; (2) reviews posted via SNIS tend to have lower ratings and be shorter than reviews posted via local accounts; (3) the channel itself, whether SNIS or a local account, does not have a direct impact on perceived review helpfulness.

Redesigned TDM-PON System Architecture Based on Point-to-Point Ethernet Transmission and Software Processing With General-Purpose Hardware

This article proposes a novel architecture of a time division multiplexing passive optical network (TDM-PON) system with general-purpose hardware. The system operates with the point-to-point (P2P) Ethernet protocol and software processing. The architecture is redesigned from the ground up, differing from conventional PON system architectures. In recent years, researchers have attempted to softwarize a number of PON functions such as media access control (MAC) and physical functions, which run on a general-purpose central processing unit (CPU). On the other hand, these existing systems still required PON-dedicated or programmable hardware for processing the PON specific protocol. This paper proposes a TDM-PON system that requires only general-purpose hardware; a network interface card with the use of the P2P Ethernet protocol and a general-purpose server with the use of software processing. To operate the proposed system, we newly developed burst-mode operation and multipoint MAC control schemes, while taking Link Fault Signaling and CPU processing jitter into account. We quantitatively evaluated the burst-mode transmission with P2P generic Ethernet TRx as a preliminary experiment, which showed that T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</sub> and T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</sub> were 75 μs and less than 1 μs, respectively. Then, we measured our developed prototype system's performance for verifying the feasibility of our proposal. In specific, it achieved a 5.2 Gbps PON system consisting of an optical line terminal (OLT) server and two optical network unit (ONU) servers under the condition of a 500-ms grant cycle including a 100-ms discovery window.

Radar Data Integrity Verification Using 2D QIM-Based Data Hiding.

The modern-day vehicle is evolved in a cyber-physical system with internal networks (controller area network (CAN), Ethernet, etc.) connecting hundreds of micro-controllers. From the traditional core vehicle functions, such as vehicle controls, infotainment, and power-train management, to the latest developments, such as advanced driver assistance systems (ADAS) and automated driving features, each one of them uses CAN as their communication network backbone. Automated driving and ADAS features rely on data transferred over the CAN network from multiple sensors mounted on the vehicle. Verifying the integrity of the sensor data is essential for the safety and security of occupants and the proper functionality of these applications. Though the CAN interface ensures reliable data transfer, it lacks basic security features, including message authentication, which makes it vulnerable to a wide array of attacks, including spoofing, replay, DoS, etc. Using traditional cryptography-based methods to verify the integrity of data transmitted over CAN interfaces is expected to increase the computational complexity, latency, and overall cost of the system. In this paper, we propose a light-weight alternative to verify the sensor data’s integrity for vehicle applications that use CAN networks for data transfers. To this end, a framework for 2-dimensional quantization index modulation (2D QIM)-based data hiding is proposed to achieve this goal. Using a typical radar sensor data transmission scenario in an autonomous vehicle application, we analyzed the performance of the proposed framework regarding detecting and localizing the sensor data tampering. The effects of embedding-induced distortion on the applications using the radar data were studied through a sensor fusion algorithm. It was observed that the proposed framework offers the much-needed data integrity verification without compromising on the quality of sensor fusion data and is implemented with low overall design complexity. This proposed framework can also be used on any physical network interface other than CAN, and it offers traceability to in-vehicle data beyond the scope of the in-vehicle applications.

Interface microstructure and strengthening mechanisms of multilayer graphene reinforced titanium alloy matrix nanocomposites with network architectures

Discontinuously reinforced 3D network structured Ti6Al4V (TC4) matrix composites with multilayer graphene (MLG) were fabricated via 3D dynamic mixing and spark plasma sintering (SPS) at high pressures (250–500 MPa). The interface microstructure, mechanical properties and strengthening mechanisms were systematically studied with various MLG contents. Experimental results exhibited that MLG can be relative uniformly dispersed onto the surface of TC4 powders by the 3D dynamic mixing method, SPS parameter of 700 °C-500 MPa caused weak interface bonding between MLG and Ti matrix, and 900 °C-250 MPa was determined as the optimal sintering condition. Appropriate ratio of in-situ generated TiC phase with approximately 30 vol% retained MLG at the interface was beneficial to the interface bonding. The compressive strength of the composites was remarkably enhanced with excellent compressive ductility. Superior mechanical properties with the highest strengthening efficiency (65.5%) and tensile strength, acceptable tensile ductility (9.0%) and higher Vickers microhardness were achieved in the 0.15 wt% MLG composites due to its better interface microstructure. The network interface strengthening mechanism by the TiC phase and residue MLG is proposed to be the dominant mechanism with a few contributions from C solid solution and fine-grain strengthening.

Advance Virtual Channel Reservation

We present a smart communication service called Advance Virtual Channel Reservation (AVCR) to provide a highway to target packets, which can greatly reduce their contention delay in NoC. AVCR takes advantage of the fact that we can know or predict the destination of some packets ahead of their arrival at the network interface (NI). Exploiting the time interval before a packet is ready, AVCR establishes an end-to-end highway from the source NI to the destination NI. This highway is built up by reserving the virtual channel (VC) resources ahead of the target packet transmission and offering priority service to flits in the reserved VC in the wormhole router, which can avoid the target packets’ VC allocation and switch arbitration delay. Additionally, optimization schemes are proposed to increase resources utilization and system performance. We evaluate AVCR with GEM5 full-system simulations by using 24 benchmarks in PARSEC and OMP2012. Compared to the state-of-art mechanisms and the priority-based mechanism, experimental results show that our mechanism can significantly reduce the target packets’ transfer latency and thus effectively decrease the average region-of-interest (ROI) time by 18.1 percent (maximally by 29.4 percent) across all benchmarks.

Research on stable control communication network platform for multi-source big data fusion

With the rapid development of large power grid, the demand for real-time, efficient and reliable applications of different services is also increasing. In this paper, based on the architecture of control and protection private network, we studied the new stable control test system and communication network scheme, proposed the multi-source data real-time transmission and interactive processing architecture of machine side, network side and load side. And we built a digital analog hybrid simulation test platform to verify its overall architecture, functions and key technologies in the test environment. Finally, we tested the reliability and rapidity indexes of communication network transmission protocol, interface and information transmission, at the same time gave some feasible suggestions for engineering application in actual power grid by taking business flow simulation as an example. It lays a foundation for engineering application and popularization.

Practical Machine Learning-Based Rate Adaptation Solution for Wi-Fi NICs: IEEE 802.11ac as a Case Study

Many rate adaptation (RA) solutions have been proposed for legacy Wi-Fi standards. However, these solutions lack extensibility, and cannot therefore be easily applied to new Wi-Fi standards such as IEEE 802.11ac. Furthermore, while several practical RA solutions have been proposed for IEEE 802.11ac network interface cards (NICs), e.g., Minstrel and Iwlwifi, they achieve poor goodput performance in some cases since they lack scalability or thoroughness. Given a large number of rates in IEEE 802.11ac, the lack of scalability results in a slow convergence on the search of the best rate over time. To accelerate the rate search, several RA solutions skip some rates to narrow down the scope of candidate rates, but lack thoroughness and may miss the best rate. Accordingly, the present study proposes a holistic RA solution, designated as Machine Learning-based RA (MLRA), which is not only practical, but also extensible, scalable and thorough. MLRA achieves the extensible property by leveraging machine learning to automatically identify the correlations among the rate, goodput performance and link quality. Moreover, it achieves the scalable and thorough properties using a two-level rate search procedure and a congestion detector. The performance of MLRA is evaluated on commodity IEEE 802.11ac NICs using TensorFlow with an asynchronous framework implemented across the kernel and user spaces of the operating system. The results show that MLRA outperforms other practical RAs by up to around 133.0%-658.0% with a negligible overhead.

Network virtualization for real-time processing of object detection using deep learning

These days, networked cameras are used in various applications using deep learning. In particular, as the deep learning technology for image processing develops, image-based application services using networked camera are expanding. Object detections are the representative application in the image-based applications. Images from the networked camera are transmitted to a deep learning machine, which performs object detection using a deep neural network (DNN) algorithm. For real-time processing of the object detection, lightweight of the image learning steps is needed. Both preprocessing of training sets and lightweight learning models can reduce computing loads for image learning. However, it is most important to receive video frames from the network camera without delay. In this paper, we provide a way for the learning machine to receive video frames with minimal delay. The proposed method is a kind of network virtualization for image-based object detection. It monitors network the status of available network interfaces in networked cameras. When a camera transmit video frames, the virtualized module determines the appropriate network interface to reduce delay. The performance of the proposed method is evaluated in the image-based object detection system using deep learning.

Impact of etcd deployment on Kubernetes, Istio, and application performance

SummaryThis experience article describes lessons learned as we conducted experiments in a Kubernetes‐based environment, the most notable of which was that the performance of both the Kubernetes control plane and the deployed application depends strongly and in unexpected ways on the performance of the etcd database. The article contains (a) detailed descriptions of how networking with and without Istio works in Kubernetes, based on the Flannel Container Networking Interface (CNI) provider in VXLAN mode with IP Virtual Server (IPVS)‐backed Kubernetes Services, (b) a comprehensive discussion about how to conduct load and performance testing using a closed‐loop workload generator, and (c) an open source experiment framework useful for executing experiments in a shared cloud environment and exploring the resulting data. It also shows that statistical analysis may reveal the data resulting from such experiments to be misleading even when careful preparations are made, and that nondeterministic behavior stemming from etcd can affect both the platform as a whole and the deployed application. Finally, it is demonstrated that using high‐performance backing storage for etcd can reduce the occurrence of such nondeterministic behaviors by a statistically significant (P &lt; .05) margin. The implication of this experience article is that systems researchers studying the performance of applications deployed on Kubernetes cannot simply consider their specific application to be under test. Instead, the particularities of the underlying Kubernetes and cloud platform must be taken into account, in particular because their performance can impact that of etcd.

Security Protection Scheme of Brain-Like System Based on Cloud Computing Platform

The functions of human being’s brains are very difficult to simulation by computers, therefore many scientists are focusing on research of brain-like system (or robotic brain) based on cloud computing platform, which can simulate the human being’s hearing, vision, thinking and action. However, due to the security incidents of the internet occur frequently, the robotic brain based on the cloud computing platform may be seriously harmed by the security incidents. Therefore, this paper designs and implements of the overall security solution of the robotic brain based on cloud platform. The cloud platform intranet is divided into the intranet trusted zone and the DMZ zone, and strict filtering strategies are set in the zone, and firewalls and firewalls are set up in the DMZ zone and the external network zone. Monitor incoming network traffic at the network interface, and deal with abnormal traffic. At the same time, it audits security events in the intranet, conducts the statistical analysis on content security, server security, and application control, and blocks suspicious attacking IP. A series of safety measures are adopted to ensure the safety of the internal network of the robotic brain based on cloud platform.

Dry Density Based on Soil Index Properties by Using Expert System

Dry density modeling is a valuable issue. Artificial neural networks (ANNs) have been used in many problems in geotechnical engineering and have demonstrated great success. In this paper, the ANN model is proposed to predict the dry density of the soil. The developed model is managed by the Matlab Neural Network Interface (R2016a). To create the ANN model, liquid limit, plastic limit, plasticity index, moisture content, specific gravity, finer accuracy than sieve 200, total suspended solids, organic and SO3 were selected and used as input parameters. There are (9, 6,5 and 3) nodes, (10) nodes and (1) node used for input, hidden layers and output layers, respectively. The value of dry density obtained from three sources was sympathetic. The first source is the experimental results of 99 soil samples conducted in Al-Najaf Institution laboratory for this study. The second source was to propose the expected dry density using multiple linear regression analysis (MLRA) on the samples used in the first source; The results show, that the prediction of the use of ANNs was closely consistent with the experimental data. Correlation coefficient (R2) and mean square error (MSE) were 0.97368 and 3.19474 10-3, respectively. The observed results of the proposed system were very comparable with those obtained from empirical analysis and the prediction obtained from multiple linear regression analysis, where the advanced ANN approach is applicable.

RDMA data transfer and GPU acceleration methods for high-throughput online processing ofserial crystallography images.

The continual evolution of photon sources and high-performance detectors drives cutting-edge experiments that can produce very high throughput data streams and generate large data volumes that are challenging to manage and store. In these cases, efficient data transfer and processing architectures thatallow online image correction, data reduction or compression become fundamental. This work investigates different technical options and methods fordata placement from the detector head to the processing computing infrastructure, taking into account the particularities of modern modular high-performance detectors. In order to compare realistic figures, the future ESRF beamline dedicated to macromolecular X-ray crystallography, EBSL8, is taken as an example, which will use a PSI JUNGFRAU 4M detector generating up to16 GB of data per second, operating continuously during several minutes. Although such an experiment seems possible at the target speed with the 100 Gb s-1 network cards that are currently available, the simulations generated highlight some potential bottlenecks when using a traditional software stack. An evaluation of solutions is presented that implements remote direct memory access (RDMA) over converged ethernet techniques. A synchronization mechanism is proposed between a RDMA network interface card (RNIC) and a graphics processing unit (GPU) accelerator in charge of the online data processing. The placement of the detector images onto the GPU is made to overlap with the computation carried out, potentially hiding the transfer latencies. As a proof of concept, a detector simulator and a backend GPU receiver with a rejection and compression algorithm suitable for a synchrotron serial crystallography (SSX) experiment are developed. It is concluded that the available transfer throughput from the RNIC to the GPU accelerator is at present the major bottleneck in online processing for SSX experiments.