Link Latency Research Articles

A predictive SD‐WAN traffic management method for IoT networks in multi‐datacenters using deep RNN

AbstractDeploying the Internet of Things (IoT) in integrated edge‐cloud environments exposes the IoT traffic data to performance issues such as delay, bandwidth limitation etc. Recently, Software‐Defined Wide Area Network (SD‐WAN) has emerged as an architecture that originates from the Software‐Defined Network (SDN) paradigm and provides solutions for networking multiple data centers by allowing network administrators to manage and control network layers. In this article, an SDWAN‐based policy for traffic management in IoT is introduced in which the Quality of Service (QoS) metrics such as end‐to‐end delay and bandwidth utilization are optimized. The proposed method implements the traffic management policy in the SDWAN controller. When the IoT traffic flows reach the SDWAN infrastructure network, graph search algorithms are performed to find the near‐optimal paths that affect the end‐to‐end delay of traffic flows. Because of the ability of deep learning to process complex data, a deep RNN model is used to predict the network state information, such as link latency and available bandwidth, before the traffic flows reach the infrastructure network. The proposed method consists of four key modules to predict the routes for future time intervals: (a) an SD‐WAN topology updater unit that checks the link changes and availability, (b) the network state information collector, which collects the network state information to create a dataset, (c) the learning unit, which trains a deep RNN model using the created dataset, and (d) the route predictor unit, which uses the trained model to predict the network state information using a heuristic algorithm to determine the routes. The simulation results showed that the deep RNN model can achieve high accuracy and low Mean Absolute Error (MAE), and the proposed method outperforms shortest‐path algorithms in terms of latency. At the same time, the available bandwidth is almost fairly distributed among all network links.

Distributed service function chaining in NFV-enabled networks: A game-theoretic learning approach

In network function virtualization (NFV), Service Function Chaining (SFC) provides an ordered sequence of virtual network functions (VNFs) and subsequent steering of traffic flows through them to cater to end-to-end services. This paper addresses the NP-hard problem of minimum cost SFC deployment to support customer services that access the carrier network’s NFV infrastructure (NFVI) through some edge routers. To determine the mappings of VNFs to physical servers, a challenging aspect would be the inter-server latencies that may fluctuate over time because of the sharing nature of cloud data centers. To construct the SFC, we come up with three different formulations, each corresponding to a different informational assumption about the link latencies: First, a centralized integer linear programming (ILP) formulation is given under the assumption of the non-causal availability of exact and instantaneous inter-server latencies. The solution to this ILP can serve as a lower bound to benchmark more scalable and realistic schemes. Next, we give a distributed game-theoretic formulation (with service broker agents as players) which only requires the statistical knowledge of link latency fluctuations. The game provably admits a pure Nash equilibrium (NE) and can be solved iteratively through the well-known best response dynamics (BRD) algorithm. Our main novelty lies in the third formulation in which each service broker has neither instantaneous nor statistical knowledge of the latencies. Instead, it relies on a game-theoretic learning algorithm to compose its VNF chain only based on its own history of adopted decisions and experienced delays on each logical link. We prove that the proposed learning algorithm asymptotically converges to NE and evaluate its performance through simulations in terms of convergence and the impact of network parameters.

Coverage Performance of Non-Lambertian Underwater Wireless Optical Communications for 6G Internet of Things

In medium- and short-range underwater application scenarios, thanks to the superior performance in transmission bandwidth, link latency, and security, underwater wireless optical communication (UWOC) is growing to be a promising complement to the mature underwater acoustic communication technique. In order to extend the future 6G Internet of Things (IOT) to various challenging and valuable underwater scenarios, the underwater spatial coverage and transmission performance has been actively discussed in typical seawater environments. However, almost all current works focus on underwater scenarios including light-emitting diode (LED) transmitters with well-known Lambertian optical beams and fail to characterize the scenarios adopting LED transmitters with distinctive non-Lambertian beam patterns. For addressing this limitation, in this article, the coverage performance of non-Lambertian UWOC for 6G is analyzed and illustrated. Furthermore, the switchable optical beam configuration scheme is proposed and estimated for UWOC. Numerical results illustrate that, compared with about 15.42 dB signal-to-noise ratio (SNR) fluctuation amplitude for UWOC with baseline Lambertian optical beam configuration, the corresponding SNR fluctuation amplitudes of UWOC based with two typical non-Lambertian optical beams are 8.71 dB and 24.60 dB. Furthermore, once the receiver depth is increased to 6.0 m, the SNR fluctuation amplitude for the above three UWOC coverage with distinct beam configuration could be reduced to 5.61 dB, 1.58 dB, and 10.33 dB, respectively.

A robust optimization approach for placement of applications in edge computing considering latency uncertainty

Edge computing brings computing and storage resources close to end-users to support new applications and services that require low network latency. It is currently used in a wide range of industries, from industrial automation and augmented reality, to smart cities and connected vehicles, where low latency, data privacy, and real-time processing are critical requirements. The latency of accessing applications in edge computing must be consistently below a threshold of a few tens of milliseconds to maintain an acceptable experience for end-users. However, the latency between users and applications can vary considerably depending on the network load and mode of wireless access. An application provider must be able to guarantee that requests are served in a timely manner by their application instances hosted in the edge despite such latency variations. This article focuses on the placement and traffic allocation problem faced by application providers in determining where to place application instances on edge nodes such that requests are served within a certain deadline. It proposes novel formulations based on robust optimization to provide optimal plans that protect against latency variations in a configurable number of network links. The robust formulations are based on two different types of polyhedral uncertainty sets that offer different levels of protection against variations in latency. Extensive simulations show that our robust models are able to keep the number of chosen edge nodes low while reducing the number of latency violations as compared to a deterministic optimization model that only considers the average latency of network links.

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network.

Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GÉANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks.

Research on Hopping Routing of Periodic Multiorbit LEO Satellites

In recent years, the low Earth orbit microsatellite network technology has experienced rapid development due to its advantages of low latency, low cost, and short development cycles. However, building an efficient and reliable satellite network routing system faces many challenges due to the characteristics of satellite networks, such as fast‐changing topology, large variations in link latency, higher probabilities of node and link failures, and limited resources. Routing algorithms have a significant impact on intersatellite communication and have become a research hotspot. The current mainstream algorithms focus on reducing information propagation delays between satellites to enable faster transmission. However, many satellite networks, such as meteorological observation satellite networks, are not sensitive to propagation delays but emphasize reducing hardware costs, especially for the receiving and transmitting signal systems of satellites. This means minimizing the single‐step signal transmission distance of satellites. This article proposes a routing algorithm based on time slot planning and the shortest path step length. Experimental simulation results demonstrate that this algorithm significantly reduces the step length of signal transmission and lowers hardware costs.

Performance evaluation of RYU controller under distributed denial of service attacks

<span>Distributed denial of service (DDoS) attacks have been identified as one of the greatest threats to software-defined networking (SDN) because they are highly effective, hard to detect, and easy to use, and they take advantage of vulnerabilities in which the new architecture still exists. This paper describes one technique for denying the RYU controller's services, which can cause the controller's resources to be depleted if a significant number of packets from various zombie hosts are sent to the controller using spoofed source internet protocol (IP) addresses. In order to demonstrate the impact of the attack, we measure various metrics related to RYU controllers such as its central processing unit (CPU) usage and network throughput. In this work, Mininet was used to simulate the data plane and measure metrics such as random access memory (RAM) usage, CPU load, and link latency.</span>

Real-Time Link Verification in Software-Defined Networks

Software-defined networking (SDN) has been widely adopted in different networks, such as datacenter and service providers. The SDN controller has the entire network view and is responsible for managing it. To obtain such a view of the network, the controller employs link discovery protocols, which are vulnerable to attacks such as link fabrication attacks (LFAs). TopoGuard and TopoGuard+ are two major systems detecting LFAs. This paper introduces a link latency attack (LLA) that can bypass the defence mechanism of both systems. LLA can poison the view of the SDN controller from the network topology and causes outages, resulting in poor quality of service (QoS) or quality of experience (QoE). To mitigate this, we develop two machine learning-based defence systems, namely machine learning-based link guard (MLLG) and real-time link verification (RLV), to preserve the required defence for LLA. The MLLG works when the network topology rarely updates, while RLV can support frequent updates. Furthermore, RLV trains itself over a link latency dataset (LLD)– including latency data of fabricated and normal links– that is captured from the ongoing packets in the network. It also implements outlier detection techniques to identify a dynamic threshold for link latency. We test both systems on different scenarios using Mininet and show that they achieve reasonable results compared with current defence algorithms. Specifically, RLV presents the highest detection performance (F1-score) to 70% at less than 0.2% false-positive rate. The system also supports the robustness features when the attack rates vary from 3% to 7% in our simulated network.

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

SummaryThe recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government‐funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard‐to‐reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications.

Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.

Slack-Aware Packet Approximation for Energy-Efficient Network-on-Chips

Network-on-Chips (NoCs) are the standard on-chip communication fabrics for connecting cores, caches, and memory controllers in multi/many-core systems. With the increase in communication load introduced by emerging parallel computing applications, on-chip communication is becoming more costly than computation in terms of energy consumption. This paper contributes to existing research on approximate communication by proposing a slack-aware packet approximation technique to reduce the energy consumed by NoCs for sustainable parallel computation. The proposed approximation technique lowers both the execution time and NoC power consumption by reducing the packet size based on slack. The slack is the number of cycles by which a packet can be delayed in the network with no effect on execution time. Thus, low-slack packets are considered critical to system performance, and prioritizing these packets during the transmission will significantly reduce execution time. The proposed technique includes a slack-aware control policy to identify low-slack packets and accelerates these packets using two packet approximation mechanisms, namely, an in-network approximation (INAP) and a network interface approximation (NIAP). INAP mechanism prioritizes low-slack packets during the arbitration phase of the router by approximating packets with high-slack. NIAP mechanism reduces the latency of the network links and switch traversals by truncating data for the low-slack packets. An approximate network interface and router are implemented to support the proposed technique with lightweight packet approximation hardware for lower power consumption and execution time. Cycle-accurate simulations using the AxBench and PARSEC benchmark suites show that the proposed approximate communication technique achieves reductions of up to 24% in execution time and 38% in energy consumption with 1.1% less accuracy loss on average compared to existing approximate communication techniques.

Swift HARQ Based on Machine Learning for Latency Minimization in URLLC

Ultra-reliable low-latency communication (URLLC) has been introduced in the 5th Generation (5G) radios for mission-critical applications that demand strict reliability and latency traffic to guarantee the rapid delivery of short packets (up to 1 ms) with a success probability rate of 99.999%. The challenging reliability and latency requirements of URLLC have significant impact on the air-interface design, especially on the Hybrid Automatic Repeat reQuest (HARQ) mechanism. This study focuses on satisfying link latency requirements by reducing the delay that arises in the presence of the HARQ operation. To this end, we propose a Swift HARQ protocol empowered by machine learning techniques to estimate the decodability of a packet early enough within its maximum number of allowable retransmission attempts. This can allow the transmitter to react faster by dropping the non-decodable packets, or activating the repetition mode where parts of the HARQ feedback can be omitted. As shown through system-level simulations, the proposed model achieves a delay reduction of more than 50% compared to the traditional HARQ, and increases the system throughput by up to 40% when multiple HARQ retransmissions are required.

Assessing Quality of Control in Tactile Cyber–Physical Systems

We evolve a methodology and define a metric to evaluate Tactile Cyber-Physical Systems (TCPS). Towards this goal, we use the step response analysis, a well-known control-theoretic method. The adoption includes replacing the human operator (or master) with a controller with known characteristics and analyzing its response to slave side step disturbances. The resulting step response curves demonstrate that the Quality of Control (QoC) metric is sensitive to control loop instabilities and serves as a good indicator of potential factors that contribute to operator-side cybersickness. Through experiments, we demonstrate how QoC accounts for network overheads such as the link latency and jitter and non-networking overheads such as the testbed settings and robot performances in a TCPS. We show that there is a one-to-one correlation between QoC and end-to-end latency, jitter, and packet drops of a TCPS implementation. We show through experiments how QoC can be used to estimate positional errors in tactile-visual control applications. Since higher positional errors can result in poor task performance, estimating them is useful in developing a better-performing TCPS. We also evaluate a TCPS using Fitts’ test and compare its results with QoC. We show that QoC is useful in distinguishing TCPS with differences in their specifications that are not detectable using Fitts’ test.

SCOPE: Cost-Efficient QoS-Aware Switch and Controller Placement in Hybrid SDN

This article addresses the joint switch and controller placement problem in a hybrid software-defined network (SDN), which is an intermediate step of transforming a traditional backbone network into a pure SDN. For a hybrid SDN, the quality of service (QoS) is a primary concern for ensuring service guarantee of a traditional network, while providing additional benefits of softwarization. In the SDN, the positions of SDN controllers determine the QoS parameters, such as network throughput and flow processing delays. Many existing solutions determine the controller locations to optimize the latency of network links and control plane load. However, there is a need to address the problem considering additional aspects of the hybrid SDN, such as SDN switch placement and incremental upgrades. In this article, we propose a priority-based SDN switch placement approach and an incremental controller placement approach for the hybrid SDN. Simulation results depict that the proposed scheme increases programmable traffic by 9.90% compared to the existing MapFirst scheme when the switch upgrade budget is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> 400 K. The proposed scheme decreases QoS violation by 20.89% compared to the Greedy approach with 0.3 million flows.

DRL-Based Low-Latency Content Delivery for 6G Massive Vehicular IoT

Vehicle-to-everything communication is an indispensable component of 6G networks that could help to facilitate future transportation systems. However, massive vehicles and unstable vehicle-to-vehicle (V2V) links may become bottlenecks for the low-latency delivery of contents, such as safety-critical emergency messages and multimedia. Instead of resolving the problem in a centralized way, we propose a massive vehicular Internet-of-Things system and investigate the approach that would enable each vehicle to decide the transmission mode from three modes, i.e., vehicle-to-network, vehicle-to-infrastructure and V2V sidelinks, and wireless resources. Specifically, a multiagent deep reinforcement learning (RL) framework is formulated by combining the multiagent RL approach, WoLF-PHC, with the techniques from deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning (DQN) to gain the formulated framework with the capability of capturing the effects of interaction between learning agents and states of complex environment. The framework is set to maximize the throughput of vehicles while maintaining the latency and reliability constraints of the vehicle communication links. However, it could be easily extended to other objectives. The simulation results demonstrate that the proposed approach outperforms the compared ones in total traffic capacity and satisfaction rate of the vehicles in communication.

AI-Based Wormhole Attack Detection Techniques in Wireless Sensor Networks

The popularity of wireless sensor networks for establishing different communication systems is increasing daily. A wireless network consists of sensors prone to various security threats. These sensor nodes make a wireless network vulnerable to denial-of-service attacks. One of them is a wormhole attack that uses a low latency link between two malicious sensor nodes and affects the routing paths of the entire network. This attack is brutal as it is resistant to many cryptographic schemes and hard to observe within the network. This paper provides a comprehensive review of the literature on the subject of the detection and mitigation of wormhole attacks in wireless sensor networks. The existing surveys are also explored to find gaps in the literature. Several existing schemes based on different methods are also evaluated critically in terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end delay. As artificial intelligence and machine learning have massive potential for the efficient management of sensor networks, this paper provides AI- and ML-based schemes as optimal solutions for the identified state-of-the-art problems in wormhole attack detection. As per the author’s knowledge, this is the first in-depth review of AI- and ML-based techniques in wireless sensor networks for wormhole attack detection. Finally, our paper explored the open research challenges for detecting and mitigating wormhole attacks in wireless networks.

Distributed competitive satellite optical burst switching mechanism for satellite networks with ultra-high link delays

The ultra-high link latency of the conventional central reservation switching method in satellite optical networks limits link utilization. In this study, a novel fully distributed competitive satellite optical burst switching (DC-SOBS) mechanism was proposed to overcome the drawbacks of the optical circuit switching method and achieve link multiplexing. Furthermore, the proposed method addressed the low link utilization problem caused by the high link latency of the central-control-related methods. The DC-SOBS mechanism in the ring network of six Geostationary Orbit (GEO) satellites improved link utilization by three orders of magnitude compared with the reservation method. A data conflict processing method was proposed to ensure that the same information was provided to various optical switching nodes, and the same algorithm was used to achieve a consistent scheduling result in each node.

Information Design in Nonatomic Routing Games With Partial Participation: Computation and Properties

We consider a routing game among nonatomic agents where link latency functions are conditional on an uncertain state of the network. The agents have the same prior belief about the state, but only a fixed fraction receive private route recommendations or a common message, which are generated by a known randomization, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">private</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">public signaling policy</i> , respectively. The remaining agents choose route according to Bayes Nash flow with respect to the prior. In this article, we develop a computational approach to solve the optimal information design problem, i.e., to minimize expected social latency over all public or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">obedient</i> private signaling policies. For a fixed flow induced by nonparticipating agents, the design of an optimal private signaling policy has been shown to be a generalized problem of moments for polynomial link latency functions, and to admit an atomic solution with a provable upper bound on the number of atoms. This implies that, for polynomial link latency functions, information design can be equivalently cast as a polynomial optimization problem. This, in turn, can be arbitrarily lower bounded by a known hierarchy of semidefinite relaxations. The first level of this hierarchy is shown to be exact for the basic two link case with affine latency functions. We also identify a class of private signaling policies over which the optimal social cost is nonincreasing with an increasing fraction of participating agents for parallel networks. This is in contrast to existing results where the cost of participating agents under a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fixed</i> signaling policy may increase with their increasing fraction.

A Link Fabrication Attack Mitigation Approach (LiFAMA) for Software Defined Networks

In software defined networks (SDNs), the controller is a critical resource, yet it is a potential target for attacks as well. The conventional OpenFlow Discovery Protocol (OFPD) used in building the topological view for the controller has vulnerabilities that easily allow attackers to poison the network topology by creating fabricated links with malicious effects. OFDP makes use of the link layer discovery protocol (LLDP) to discover existing links. However, the LLDP is not efficient at fabricated link detection. Existing approaches to mitigating this problem have mostly been passive approaches that depend on observing unexpected behaviour. Examples of such behaviour include link latency and packet patterns to trigger attack alerts. The problem with the existing solutions is that their implementations cause longer link discovery time. This implies that a dense SDN would suffer from huge delays in the link discovery process. In this study, we propose a link fabrication attack (LFA) mitigation approach (LiFAMA), which is an active mitigation approach and one that minimises the link discovery time. The approach uses LLDP packet authentication together with keyed-hash-based message authentication code (HMAC) and a link verification database (PostgreSQL) that stores records of all known and verified links in the network. This approach was implemented in an emulated SDN environment using Mininet and a Python-based open-source OpenFlow (POX) controller. The results show that the approach detects fabricated links in an SDN in real time and helps mitigate them. Additionally, the link discovery time of LiFAMA out-competes that of an existing LFA mitigation approach.

Two-Timescale Resource Management for Ultrareliable and Low-Latency Vehicular Communications

Ultra-reliable low-latency communication (URLLC) is essential for future vehicle-to-vehicle (V2V) networks to improve traffic safety and enhance driving experience. Due to the fast-varying channel caused by high mobility, guaranteeing latency and reliability performance of the V2V links is a tremendous challenge. In this paper, we propose a novel resource allocation framework to support ultra-reliable low-latency V2V communications. The proposed framework includes both large-scale and small-scale resource optimizations. The large-scale resource allocation is performed at the central base station based on large-scale channel information periodically collected from vehicles. On the other hand, the small-scale resource allocation is performed at the vehicles according to instantaneous channel and queuing information. We develop optimal solutions for both resource allocation problems. With the proposed optimal solutions, the latency performance at the occurrence of extreme events is enhanced by enabling spectrum sharing among the vehicles. Simulation results demonstrate that the proposed algorithm can effectively improve the URLLC performance compared against the benchmark algorithm.