Link Delay Research Articles

P4dm: An Effective Approach to Link Delay Measurement with P4

P4dm: An Effective Approach to Link Delay Measurement with P4

Adaptive-Sunflower-Based Grey Wolf Algorithm for Multipath Routing in IoT Networks

This paper devises a routing method for providing multipath routing in an IoT network. Here the fractional artificial bee colony(FABC) algorithm is devised for initiating the clustering process. Moreover, the multipath routing is performed by the newly devised optimization technique, namely adaptive-sunflower-based grey wolf (adaptive-SFG) optimization technique, which is designed by incorporating adaptive idea in sunflower-based grey wolf technique. In addition, the fitness function is newly devised by considering certain factors that involve context awareness, link lifetime energy, trust, and delay. For the computation of the trust, additional trust factors like direct trust, indirect trust, recent trust, and forwarding rate factor are considered. Thus, the proposed adaptive-SFG algorithm selects the multipath for routing based on the fitness function. Finally, route maintenance is performed to ensure routing without link breakage. The proposed adaptive-SFG outperformed other methods with high energy of0.185J minimal delay of 0.765 sec maximum throughput of 47.690% and maximum network lifetime of 98.7%.

Optimal Estimation of Link Delays Based on End-to-End Active Measurements

Current IP-based networks support a wide range of delay-sensitive applications such as live video streaming of network gaming. Providing an adequate quality of experience to these applications is of paramount importance for a network provider. The offered services are often regulated by tight Service Level Agreements (SLAs) that needs to be continuously monitored. Since the first step to guarantee a metric is to measure it, delay measurement becomes a fundamental operation for a network provider. In many cases, the operator needs to measure the delay on all network links. We refer to the collection of all link delays as the Link Delay Vector (LDV). Typical solutions to collect the LDV impose a substantial overhead on the network. In this paper, we propose a solution to measure the LDV in real-time with a low-overhead approach. In particular, we inject some flows into the network and infer the LDV based on the delay of those flows. To this end, the monitoring flows and their paths should be selected minimizing the network monitoring overhead. In this respect, the challenging issue is to select a proper combination of flows such that by knowing their delay it is possible to solve a set of linear equations and obtain a unique LDV. This combination of monitoring flows should be optimal according to some criteria and should meet some feasibility constraints. We first propose a mathematical formulation to select the optimal combination of flows, in form of an Integer Linear Programming (ILP) problem. Then we develop a heuristic algorithm to overcome the high computational complexity of existing ILP solvers. As a further step, we propose a meta-heuristic algorithm to solve the above-mentioned equations and infer the LDV. The challenging part of this step is the volatility of link delays. The proposed solution is evaluated over real-world emulated network topologies using the Mininet network emulator. Emulation results show the accuracy of the proposed solution with a negligible networking overhead in a real-time manner.

Large-Scale 3D Baseline Measurement Based on Phase-Stabilized GNSS-Over-Fiber System

Multi-antenna GNSS-over-fiber system is considered an effective solution for three-dimension (3D) baseline measurement. By precisely determining the transmission time delays between the antennas and the receiver, the vertical precision can be improved based on the single difference (SD) model. This method, however, would encounter problems if the baseline is too long, since a high precision measurement of the time delay of a long fiber link is usually time-consuming. Here, we propose and demonstrate a high-speed phase-stabilized GNSS-over-fiber system for large-scale 3D baseline measurement. A relatively slow but accurate time delay measurement module is used to calibrate the link delay, then a fast active compensation device is employed to keep the time delay constant. If the delay variation exceeds the compensation range, the time delay measurement module would measure the link delay again and bias the active compensation device at a new operating state. In a proof-of-concept experiment, the precision of the 3D baseline measurement obtained by the proposed system with a 20-km optical fiber is around 2.82 mm thanks to the rigid active compensation and the high-precision time delay measurement. If the active compensation device is disabled, the vertical precision of the baseline measurement obtained would be degraded from 2.82 mm to 16.46 mm in 10 minutes.

Computation of time-delay for Delayed Network Controlled Temperature Control System

This paper presents an Investigation on the stability of network controlled temperature control system having Time-Invariant feedback delays, by utilizing a direct method for TDS stability analysis. A PI controller based stability analysis for temperature control system with Time invariant feedback loop delay has been constructed in this paper. The stability problem has been formulated based on the transfer function model of the closed loop system with various time delays. For different subsets of the controller parameters, based on the stability criterion’s maximal permissible bound of the network link delay that the closed loop system can accommodate without losing the stability has been computed. The effectiveness of the obtained result was validated on a benchmark temperature control system using MATLAB simulation software.

Risk-aware optimized quickest path computing technique for critical routing services

In this paper, a novel model has been proposed for the computation of optimized path with link reliability, delay and capacity. Algorithms have been proposed for the optimized data path with a single link-weight and it constitutes two main parts: (i) characterization of the network for the best optimized path, and (ii) tuning of the network parameters for the prediction/validation for the Best Optimized Path, according to a given data flow. It also needs very little a priori information during the online computation as the computed optimized path requires less computational overhead as each data flow shall be assigned with a precomputed and optimized link weight. Now, with the help of this optimized link weight one can find the optimum path with respect to both risk and lag-time in an online approach simply by adopting the Dijkstra's algorithm. This approach has been illustrated here in this paper with the help of two different topologies for different values of data flow from single source to single destination. Algorithms have been proposed based on this mathematical model. It also can be observed that proposed algorithms are able to compute the optimized path with having complexity of Dijkstra's algorithm when computer together. This study can be claimed that the proposed algorithms are self-sufficient to provide the risk-aware quickest path routing, even without having any prior information on the path to be allocated. One can easily extend it for the case of many source nodes to the many destination nodes as well as for the software defined networking (SDN) in the future work.

Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment

With the growth of Internet of Things (IoT) services and applications, the efficient transmission of IoT data has been crucially required. The IETF has recently developed the QUIC protocol for UDP-based multiplexed and secure transport. The Multipath QUIC (MP-QUIC) is also being discussed as an extension of QUIC in the multipath network environment. In this paper, we propose a proxy-based adaptive MP-QUIC transmission for throughput enhancement in the IoT environment. In the proposed scheme, a proxy device is employed between IoT clients and IoT server to aggregate the traffics of many clients in the access network. The proxy will transport a large among of traffics to the server, adaptively to the network conditions, by using multiple paths in the backbone network. For this purpose, the proxy device employs a path manager to monitor the current network conditions and a connection manager to manage the MP-QUIC connections with the IoT server over the backbone network with multiple paths. For effective MP-QUIC transmission, the proxy will transmit the prioritized packets to the server using the best path with the lowest round-trip time (RTT), whereas the non-prioritized packets are delivered over the other paths for traffic load balancing in the network. From the testbed experimentations with the MP-QUIC implementation and ns-3 simulation modules, we see that the proposed scheme can outperform the normal QUIC (using a single path) and the existing MP-QUIC scheme (using the round-robin policy) in terms of response delay and total transmission delay. Such performance gaps tend to increase as the link delays and packet loss rates get larger in the network.

Disturbance Observer and Tube-Based Model Predictive Controlled Electric Vehicles for Frequency Regulation of an Isolated Power Grid

This paper presents a frequency regulation approach applied to a renewable penetrated isolated power grid in the presence of a large number of Electric Vehicles (EVs). A disturbance observer is initially designed for a reduced-order model of the system to produce a supplementary frequency control signal for the aggregated EVs. The reduced order model is obtained by combining the variations in the load, wind, photovoltaic systems, and aggregated EVs in order to yield a lumped disturbance to be estimated by the proposed disturbance observer. A tube-based Model Predictive Control (MPC) is then proposed to provide efficient control signals for improving the response of the aggregated EVs. The control signals are produced so as to obtain the least value of frequency deviation error with a minimum control effort while taking a variety of operational and physical constraints into consideration. The proposed control strategy offers the capability to cope with the uncertainties caused by the external disturbances by coordinating the charging and discharging of aggregated EVs. The impact of the delay in communication links is also investigated by conducting a stability analysis to obtain the delay margin. Simulations are conducted on an isolated system to illustrate the effectiveness of the designed observer, and also to examine the advantage of the presented tube-based MPC over a conventional MPC, a fuzzy proportional-integral control, and a linear quadratic regulator control.

Accurate Single-Ended Measurement of Propagation Delay in Fiber Using Correlation Optical Time Domain Reflectometry

A correlation optical time-domain reflectometry (COTDR) method is presented, which measures the propagation delay with an accuracy of a few picoseconds. This accuracy is achieved using a test signal data rate of 10 Gbit/s and employing cross-correlation and pulse fitting techniques. In this paper we introduce and evaluate the basic signal processing steps, investigate the measurement accuracy, and discuss applications for monitoring link delay and chromatic dispersion of long fiber spans as well as temperature sensing applications.

Traffic Allocation for Heterogeneous Links in Satellite Data Relay Networks

Efficient traffic allocation for heterogeneous links in satellite data relay networks (SDRNs) has been an attractive topic nowadays. However, different traffic types and link heterogeneity bring plenty of difficulties to the link delay evaluation which directly impacts the traffic allocation efficiency. Owing to the unique determination of the probability distribution of arrival processes, we succeed in characterizing any type of traffic with the help of the moment generating function (MGF). According to the alternation and discontinuity of satellite ground links (SGLs), we propose a Markov chain Monte Carlo embedded service matrix construction method to model the SGL service. Being apt at combining service processes, the stochastic network calculus based min-plus convolution is employed to depict the relay service. By deriving the tight upper bounds of the delay in heterogeneous links based on the MGF, min-plus convolution, and Markov chain, respectively, taking advantage of the Chernoff bound and Union bound, we calculate the optimal traffic allocation ratio in terms of minimum system delay. Finally, numerous simulations are conducted to show the relationships among the optimal traffic allocation ratio, traffic type, and link heterogeneity.

The influence of link characteristics on road traffic noise mapping by using Big Data

This paper describes a new method developed within the BEEP project (Big data for Environmental and occupational EPidemiology) to estimate road traffic flows and to improve the truthfulness of noise maps for agglomerations through Big Data treatment. This new approach, based on data provided by Google API, acquires information regarding travel time to estimate traffic volumes using link delay functions. To achieve this goal, an appropriate experimental plan was designed to simultaneously collect travel times by Google Application Programming Interface (API) and traffic volumes on site. The experimental survey, carried out in the cities of Rome and Pisa, involved different types of road links with traffic lights or roundabouts and different number of lanes. The influence of link characteristics on the correlation between travel time and traffic flow was analysed. The method developed was used in a small area of the city of Rome, and noise maps derived by Big Data were compared to noise maps produced via conventional means.

Kalman prediction-based virtual network experimental platform for smart living

Today, the increasing investigation of smart living opens new attack surfaces to intruders. Therefore, developing an emulation platform to test new technologies for current large-scale smart living scenarios is an emerging issue. However, existing network emulation systems suffer from coarse-grained virtual link emulation, including bandwidth, delay, and packet loss rate (PLR). Furthermore, a lack of virtual link emulation accuracy results in an unpredictable emulation fidelity. In order to address this issue, this paper presents Kalman prediction-based experimental platform based on multiple virtualization technologies for smart living to test new technologies and security threats. We introduce the core deployment, link-emulation, and emulator modules in the framework and propose Delay-PLR Kalman Prediction (DPKP) algorithm, which employs Kalman prediction theory based on the packet delay and loss measurement scheme. In particular, the DPKP takes the inherent network traffic errors of the substrate network into consideration, thus improving emulation fidelity. Experiments on the emulation of a BeiDou based satellite network scenario verify the ability of our emulation system to flexibly construct a target virtual network and set the expected bandwidth for each virtual link within 5% errors. In addition, the experimental results demonstrate that the proposed DPKP-based virtual link emulation scheme outperforms the traditional method by up to 21.54% and 78.65% in terms of delay and PLR, respectively.

Improved algorithm of cloud service node path based on cross-border transaction platform under load balancing

Cross-border trading platforms study logistics from the perspective of the network, involving all links in the network and the supply chain. The network status is constantly changing, and the acquisition and analysis of the real-time status of each link on the transaction path can further improve the accuracy of the optimal path selection. Meanwhile, traditional networks can no longer adapt to the rapid development of today’s network technology and applications due to their rigid structure and simplified functions. As for traditional networks and their associated network equipment, problems such as inconvenient management and monitoring, poor interoperability, difficulty in expanding network functions, and high operating costs have severely hindered the efficiency of network functions and the pace of innovation. In order to solve many problems faced by traditional networks, in the paper, an SDN network path selection algorithm based on the dual impact factors of cloud service nodes is proposed. In the algorithm proposed in the paper, the final selected path is considered to be composed of multiple links. First, a cloud environment based on the Ryu controller is build, and a corresponding data collection strategy is constructed to complete the acquisition of the link available bandwidth and delay that affect the real-time status of the path. Then, the weighted results are calculated according to the proposed candidate path weight distribution formula to realize the sorting and screening of each transaction link, and provide support for the selection of the cross-border transaction system service path with load balance. The simulation results show that compared with the traditional DLB algorithm, the algorithm proposed in the paper has better load balancing performance, which further shows that the proposed algorithm has a better choice for the path.

Remote Fault-Tolerant Control for Industrial Smart Surveillance System

We design a remote fault-tolerant control for an industrial surveillance system. The designed controller simultaneously tolerates the effects of local faults of a node, the propagated undesired effects of neighboring connected nodes, and the effects of network-induced uncertainties from a remote location. The uncertain network-induced time delays of communication links from the sensor to the controller and from the controller to the actuator are modeled using two separate Markov chains and packet dropouts using the Bernoulli process. Based on linear matrix inequalities, we derive sufficient conditions for output feedback-based control law, such that the controller does not directly depend on output, for stochastic stability of the system. The simulation study shows the effectiveness of the proposed approach.

Delay-Aware Control Plane Virtual Topology Design of Software Defined-Elastic Optical Network

An upper bound on the delay in the control channels is necessary as control state inconsistency significantly degrades the performance of a logically-centralized and physically-distributed control plane. Statistically multiplexing control traffic with the data traffic (in-fiber in-band) or reserving a low-rate control channel does not ensure QoS guarantees in terms of delay specially if the network is congested. Therefore, control traffic must be viewed and treated differently from data traffic. This article presents ac MILP and a heuristic to design distributed SDN-based in-fiber out-of-band control plane for elastic optical network (EON). The objective is to minimize the number of controllers while reserving resources for the control traffic to ensure that delay in the control channel is bounded. The designed inter-controller virtual tree topology avoids control traffic replication. For better resource utilization it requires OEO conversion at every controller node for grooming control traffic and this incurs queuing delay at each controller node. The design takes into account queuing delay along with transmission and propagation delay in control plane links and maintains the total delay within a specified bound. To the best of our knowledge, this is the first work to analyze queuing delay along with transmission and propagation delay in inter-controller links of the control plane. We further present a MILP to make this virtual control plane tree topology survivable against single link failure by utilizing the concept of fundamental cycles. The numerical results show that our proposed control plane design performs better than existing control plane designs where control traffic is statistically multiplexed with data traffic in terms of delay in the control plane and blocking probability in the data plane.

Estimating the external costs of travel on GPS tracks

Providing people with information on the external costs of their mobility, including generated emissions and congestion, has been shown to influence travel behaviour. We present a methodology for estimating transport externalities at the link level on GPS data, with the aim to provide more accurate estimates of external costs. Emission values for various pollutants are calculated for each link using the HBEFA database, accounting for vehicle type, road category and traffic conditions. For congestion, average link delays are computed for each hour of the day. We apply this methodology to GPS trips collected during a national mobility pricing study. The methodology is validated against the Swiss national estimates. The range of per-kilometer values for various external costs are compared to the Swiss norm values. The observed trip-level heterogeneity in per-kilometer external costs supports the hypothesis that using average values in the analysis of external costs is insufficient.

Increasing fault tolerance of data plane on the internet of things using the software-defined networks.

Considering the Internet of Things (IoT) impact in today’s world, uninterrupted service is essential, and recovery has received more attention than ever before. Fault-tolerance (FT) is an essential aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in systems. The advent of software-defined networking (SDN) in the IoT plays a significant role in providing a reliable communication platform. This paper proposes a data plane fault-tolerant architecture using the concepts of software-defined networks for IoT environments. In this work, a mathematical model called Shared Risk Link Group (SRLG) calculates redundant paths as the primary and backup non-overlapping paths between network equipment. In addition to the fault tolerance, service quality was considered in the proposed schemes. Putting the percentage of link bandwidth usage and the rate of link delay in calculating link costs makes it possible to calculate two completely non-overlapping paths with the best condition. We compare our two proposed dynamic schemes with the hybrid disjoint paths (Hybrid_DP) method and our previous work. IoT developments, wireless and wired equipment are now used in many industrial and commercial applications, so the proposed hybrid dynamic method supports both wired and wireless devices; furthermore multiple link failures will be supported in the two proposed dynamic schemes. Simulation results indicate that, while reducing the error recovery time, the two proposed dynamic designs lead to improved service quality parameters such as packet loss and delay compared to the Hybrid_DP method. The results show that in case of a link failure in the network, the proposed hybrid dynamic scheme’s recovery time is approximately 12 ms. Furthermore, in the proposed hybrid dynamic scheme, on average, the recovery time, the packet loss, and the delay improved by 22.39%, 8.2%, 5.66%, compared to the Hybrid_DP method, respectively.

Efficient Delay Minimization Algorithm for Cache-Enabled NOMA Systems

This letter adopts a proactive mechanism, namely, recommendation, to boost the performance of cache-enabled non-orthogonal multiple access (NOMA) systems from a delay minimization perspective. To this end, the impact of recommendation is first explored on the average latency of a cache-aware NOMA system, which consists of two components, namely the backhaul link delay and the content delivery delay. Consequently, the closed-form expressions regarding the latency-preserving capability of cache-assisted NOMA with recommendation are derived compared to the conventional orthogonal multiple access (OMA) strategy without recommendation. Thereafter, the associated recommendation decision making problem is optimally solved by a genetic algorithm (GA)-assisted approach. Monte Carlo simulation results validate the superiority of our proposed scheme compared to a wide range of benchmarks.

Cooperative Learning for Disaggregated Delay Modeling in Multidomain Networks

Accurate delay estimation is one of the enablers of future network connectivity services, as it facilitates the application layer to anticipate network performance. If such connectivity services require isolation (slicing), such delay estimation should not be limited to a maximum value defined in the Service Level Agreement, but to a finer-grained description of the expected delay in the form of, e.g., a continuous function of the load. Obtaining accurate end-to-end (e2e) delay modeling is even more challenging in a multi-operator (Multi-AS) scenario, where the provisioning of e2e connectivity services is provided across heterogeneous multi-operator (Multi-AS or just domains) networks. In this work, we propose a collaborative environment, where each domain Software Defined Networking (SDN) controller models intra-domain delay components of inter-domain paths and share those models with a broker system providing the e2e connectivity services. The broker, in turn, models the delay of inter-domain links based on e2e monitoring and the received intra-domain models. Exhaustive simulation results show that composing e2e models as the summation of intra-domain network and inter-domain link delay models provides many benefits and increasing performance over the models obtained from e2e measurements.

Integration of Emergency Response Management System with Internet of Things

Disasters can be classified either from mother nature such as landslide, earthquake, tsunami and lightning or cause by human such as leakage in gas production or terrorist attacks. Disaster leads to huge destruction economically and affected human lives. Whilst various technologies available towards disaster analysis occurrence, the Internet of Things (IoT) offers reliable solutions in handling the relevant disasters situation, such as early warning system, real time data analytics, remote monitoring and search and rescue localization. This research developed an integration of emergency response management system with IoT. The system integrates heterogeneous wireless-enabled embedded controller with sensor, smart phones, desktop and wireless communication for end-to-end network connectivity which is monitored by a cloud platform. The system performance was evaluated in terms of the end-to-end link quality estimation and delay.