Link State Information Research Articles

Link-Quality Aware Routing for Software-Defined Vehicular Networks

The routing protocol for Vehicular Ad Hoc Networks (VANETs) uses hop-count as a routing metric to determine the shortest route from source to destination. The presence of unstable links in the selected route may cause packet failures, resulting in an increase in packet retransmissions, causing network congestion, increasing total end-to-end delay, and reducing throughput. Thus, route selection based on hop count may not always be optimal in terms of route link quality. In Software-Defined Vehicular Networks (SDVN), the global view of link-state information for the vehicular networks is created at the controller; thus, a better route can be computed. Our study proposes an SDVN routing technique that considers link quality while determining the route. Our proposed framework examines the link quality using two metrics: Expected Transmission Count (ETX) and Expected Transmission Time (ETT), and this information is used by the controller to compute the flow rule. The routing performance of the proposed scheme is compared to traditional VANETs and other existing works with the help of network simulations using the NS-3 simulator. The obtained simulation results for our proposed scheme outperform in terms of performance metrics such as packet delivery ratio, average throughput, and end-to-end delay.

Software Defined Network-Based Multi Service Routing Algorithm for Spatial Information Network

In order to solve the problems of unbalanced utilization of link resources due to the different capabilities of nodes in the spatial information network and the difficulty of satisfying the quality of service(QoS) with the change of task requirements, this paper proposes a multi-service routing algorithm for a spatial information network based on Software Defined Network(SDN), which divides the network transmission services into three categories: delay sensitive, bandwidth sensitive and packet loss rate sensitive. The link state information between satellite nodes is collected by an SDN controller, and then the topology-based bidirectional search algorithm is used to establish a “Source-Destination” set of routes between satellite nodes, the algorithm counts the delay, bandwidth, and packet loss rate of each route in the set, and selects routes that meet the needs of different business classes for data forwarding. The results show that the routing cost of this algorithm is lower than other algorithms, and it has better throughput performance.

Task offloading mechanism based on federated reinforcement learning in mobile edge computing

With the arrival of 5G, latency-sensitive applications are becoming increasingly diverse. Mobile Edge Computing (MEC) technology has the characteristics of high bandwidth, low latency and low energy consumption, and has attracted much attention among researchers. To improve the Quality of Service (QoS), this study focuses on computation offloading in MEC. We consider the QoS from the perspective of computational cost, dimensional disaster, user privacy and catastrophic forgetting of new users. The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning (FL) adaptive task offloading algorithm in MEC. The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay. To solve the problems of privacy and catastrophic forgetting, we use FL to make distributed use of multiple users’ data to obtain the decision model, protect data privacy and improve the model universality. In the process of FL iteration, the communication delay of individual devices is too large, which affects the overall delay cost. Therefore, we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL. The simulation results indicate that compared with existing schemes, the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.

An effective prediction method for network state information in SD-WAN

In a software-defined wide area network (SD-WAN), a logically centralized controller is responsible for computing and installing paths in order to transfer packets among geographically distributed locations and remote users. Accordingly, this would necessitate obtaining the global view and dynamic network state information (NSI) of the network. Therefore, the centralized controller periodically collects link-state information from each port of each switch at fixed time periods. While collecting NSI in short periods causes protocol overhead on the controller, collecting in longer periods leads to obtaining inaccurate NSI. In both cases, packet losses are inevitable, which is not preferred for quality of service (QoS). Packet loss needs to be reduced by minimizing the protocol overload on the controller and collecting accurate NSI to provide better QoS. This work proposes an effective prediction method for collecting NSI (PM-NSI) that significantly reduces packet loss and controller protocol load allowing the controller to collect accurate NSI in longer periods. The proposed method is compared against the existing NSI collection method, which collects NSI periodically, in use on the RYU controller and the Mininet emulator by using a dynamic routing algorithm. The test results indicated that PM-NSI reduces controller load around 1000% by collecting NSI in longer periods and so outperforms the existing periodic NSI collection method in terms of packet loss, jitter, controller load, and thus QoS.

A novel security mechanism for software defined network based on Blockchain

The decoupling of the data plane and the control plane in the Software- Defined Network (SDN) can increase the flexibility of network management and operation. And it can reduce the network limitations caused by the hardware. However, the centralized scheme in SDN also can introduce some other security issues such as the single point of failure, the data consistency in multiple-controller environment and the spoofing attack initiated by a malicious device in the data plane. To solve these problems, a security framework for SDN based on Blockchain (BCSDN) is proposed in this paper. BCSDN adopts a physically distributed and logically centralized multi-controller architecture. LLDP protocol is periodically used to obtain the link state information of the network, and a Merkle tree is establised according to the collected link information and the signature is generate based on KSI for each link that submitted by a switch by the main controller selected by using the PoW mechanism. Such, the dynamic change of network topology is recorded on Blockchian and the consistency of the topology information among multiple controllers can be guaranteed. The main controller issues the signature to the corresponding switch and a controller checks the legitimate of a switch by verifying the signature when it requests the flow rule table from the controller later. The signature verification ensures the authenticated communication between a controller and a switch. Finally, the simulation of the new scheme is implemented in Mininet platform that is a network emulation platform and experiments are done to verify our novel solution in our simulation tool. And we also informally analysis the security attributes that provided by our BCSDN.

A dynamic source routing protocol based on path reliability and link monitoring repair.

The two most essential factors for mobile self-organizing networks applicable to drones are reliability and stability. In harsh communication environments, such as mountainous regions and natural disasters, the use of satellites and terrestrial communication stations has severe time delays due to the high speed of UAVs, resulting in frequent communication interruptions with UAVs. Therefore, UAVs need to establish self-organizing networks for communication and information sharing. High-speed movement will lead to rapid changes in the network topology, resulting in established links being in an unstable connection state and even frequent routing errors, thus preventing the establishment of stable communication links. In order to improve the communication quality of UAVs under high-speed movement, we propose a dynamic source routing protocol based on path reliability and monitoring repair mechanism (DSR-PM). The model performs data transmission by filtering the best reliability path. The link state information is monitored during transmission and broken links are repaired in time to ensure the communication stability and reliability of the links and improve the data transmission efficiency. We simulated the approach in NS2 software and the simulation results show that the DSR-PM protocol effectively reduces parameters such as overhead, packet loss and delay, improves network throughput, and provides better communication performance.

Closer: Scalable load balancing mechanism for cloud datacenters

Cloud providers (e.g., Google, Alibaba, Amazon) own large-scale datacenter networks that comprise thousands of switches and links. A load-balancing mechanism is supposed to effectively utilize the bisection bandwidth. Both Equal-Cost Multi-Path (ECMP), the canonical solution in practice, and alternatives come with performance limitations or significant deployment challenges. In this work, we propose Closer, a scalable load balancing mechanism for cloud datacenters. Closer complies with the evaluation of technology including the deployment of Clos-based topologies, overlays for network virtualization, and virtual machine (VM) clusters. We decouple the system into centralized route calculation and distributed route decision to guarantee its flexibility and stability in large-scale networks. Leveraging In-band Network Telemetry (INT) to obtain precise link state information, a simple but efficient algorithm implements a weighted ECMP at the edge of fabric, which enables Closer to proactively map the flows to the appropriate path and avoid the excessive congestion of a single link. Closer achieves 2 to 7 times better flow completion time (FCT) at 70% network load than existing schemes that work with same hardware environment.

Research on QoS Centralized Routing of Software-Defined Aeronautic Swarm Network

To address the problem of unstable data transmission caused by frequent topology changes in Aeronautic Swarm Network (ASNET), a centralized routing algorithm for Quality of Service(QoS) of the aeronautic swarm network based on Software Defined Network(SDN) is studied. First, this paper designs a Software Defined Aeronautic Swarm Network (SD-ASNET) model. Then, taking advantage of the SDN controller’s ability to obtain global link-state information, we propose a Multiple Link-state Awareness routing (MLSA) based on the shortest path algorithm, taking into account link bandwidth, transmission delay and other factors. The simulation results show that the proposed centralized routing protocol makes full use of the centralized decision-making capability of the SDN controller, and effectively optimizes the link bandwidth utilization and transmission delay by using link parameter information.

A Trusted NUMFabric Algorithm for Congestion Price Calculation at the Internet-of-Things Datacenter

The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information. However, it is difficult to predict the congestion state of the link-end accurately at the source. In this paper, we presented an improved NUMFabric algorithm for calculating the overall congestion price. In the proposed scheme, the whole network structure had been obtained by the central control server in the Software Defined Network, and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated. In this scheme, the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information. Based on the historical data, the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm. The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm. We evaluate this evolved NUMFabric algorithm in NS3, which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.

Intelligent Routing Based on Reinforcement Learning for Software-Defined Networking

Traditional routing protocols employ limited information to make routing decisions, which can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality of Service (QoS) requirements of applications. This article introduces a novel approach for routing in Software-defined networking (SDN), called Reinforcement Learning and Software-Defined Networking Intelligent Routing (RSIR). RSIR adds a Knowledge Plane to SDN and defines a routing algorithm based on Reinforcement Learning (RL) that takes into account link-state information to make routing decisions. This algorithm capitalizes on the interaction with the environment, the intelligence provided by RL and the global view and control of the network furnished by SDN, to compute and install, in advance, optimal routes in the forwarding devices. RSIR was extensively evaluated by emulation using real traffic matrices. Results show RSIR outperforms the Dijkstra's algorithm in relation to the stretch, link throughput, packet loss, and delay when available bandwidth, delay, and loss are considered individually or jointly for the computation of optimal paths. The results demonstrate that RSIR is an attractive solution for intelligent routing in SDN.

Analisis Routing Protokol Optimized Link State Routing (OLSR) Pada Raspberry Pi

Optimized Link State Routing (OLSR)is one of the two standard for mesh networks. OLSR is a link state routing protocol, which use hallo message and topology control (TC) to determine the link state information across the mobile ad-hoc network. OLSR is widely used in the wireless mesh network. Raspberry Pi is a mini computer which can be used as a node router to replace the function of a router in a wireless mesh network. In this research, Raspberry Pi is used as a router nodes to determine the performance of routing protocol OLSR. The parameters used to measure its performance is self-configure time, self-healing and bandwidth usage. The test results showed that the use of routing protocols OLSR on the Raspberry Pi in a wireless mesh network proved able to repair itself if there is a problem on the network since it has ability to self-configure and self-healing.

Control of Flexible Joint Robot Based on Motor State Feedback and Dynamic Surface Approach

A controller based on dynamic surface control and observer is proposed by using motor state feedback for trajectory tracking of flexible joint robot with uncertain link dynamic model. Considering the link state information cannot be obtained, an observer is designed to estimate the link state information, and a dynamic surface controller is proposed based on link state observer. The controller based on the observer compared to backstepping controller avoids the repeated differentiation problem. At the same time, the dynamic surface method avoids the measurement of high order signal. The simulation results show that the designed controller has a good trajectory tracking effect, which effectively suppresses the residual vibration of the flexible joint robot. Moreover, the proposed controller and observer are robust to the uncertainty and external disturbance of the link dynamic model. The proposed controller can be directly applied in the flexible joint robot without installing additional sensors, which is very important for industrial applications.

Multi Point Relay Selection in OLSR Routing Protocol using Particle Swarm Optimization

Mobile ad hoc networks (MANETs) or wireless ad-hoc sensor network are capable of configuring, organizing networks with ad-hoc infrastructure. In MANETs nodes are mobile therefore the topology of MANETS changes dynamically. The nodes in MANETS are with limited transmission range so they cannot communicate directly so uses the intermediate nodes for communication. As nodes in a MANET normally have limited transmission ranges, some nodes cannot communicate directly with each other. Hence uses other intermediate nodes for communication so every node also work as a router Various routing protocols are available for MANETS. Optimized Link State routing protocol (OLSR) is a table driven, proactive protocol derived from optimization of the link state routing protocol. It uses hello and topology control messages to find out and then distribute link state information throughout the mobile ad hoc network. It is a congestion control routing protocol, which uses the concept of flooding through Multi Point Relay (MPR) only to minimize the overhead of routing messages.

Spectrum Detection and Link Quality Assessment for Heterogeneous Shared Access Networks

The persistent awareness of dynamic links quality is of critical importance to shared access systems, e.g., promoting sensing accuracy or even enabling seamless sharing, which remains yet as a challenging task. In this paper, we consider the shared access in heterogeneous small-cells, which are likely to be the main infrastructure components by utilizing the carrier aggregation (CA) scheme, potentially combining both licensed and unlicensed bands. We suggest a promising sensing scheme, which permits the simultaneous assessment of multiple links quality when detecting vacant spectrum, and hence is able to solve the long-standing hidden terminal problem. We incorporate an additional objective of acquiring dynamic link state information of multiple incumbent transmitters/receivers, and thereby, structure spectrum sensing as a mixed classification and estimation problem. The main challenge is that multiple channels may become similar with each other, and existing joint estimation schemes are unable to distinguish which incumbent is active and what its associated channel state is. To address this indistinguishable problem and alleviate the error propagation in recursive estimations, a novel alternating Bernoulli filtering algorithm is proposed. By fully exploiting the group transitional behavior of incumbents, a multiple-chains inference framework is developed. Numerical simulations validate our new scheme. Occupancy states of multiple incumbents can be identified accurately, by tracking their links quality and exploiting the underlying dynamics, which lays the foundation for more effective shared access.

LAPGN: Accomplishing information consistency under OSPF in General Networks (an extension)

Open Shortest Path First (OSPF) protocol is a link-state routing protocol which requires the link-state information to be synchronous, and it needs to achieve a fast convergence when the network topology changes. In this paper, we extend the Limitation Arrangement Principle (LAP) algorithm to General Networks (GN) and design an LAP-based routing algorithm in GN (LAPGN) to solve the information consistency problem of the full network under OSPF with the following operations: (i) decomposing GN into one or more Single-link Networks (SNs) with the approach of depth-first walk; (ii) re-composting the SNs to a network with regular topology structure by connecting networks and adding links; (iii) searching the undirected complete graph of three nodes round by round until it converges to a simple network topology based on region binding; and (iv) processing different converged network topologies with different LAP-based routing algorithms. We compare the proposed algorithm with the well-known OSPF algorithm over some random network topologies and six backbone topologies. The simulation results reveal that LAPGN can solve the information consistency problem and has better performance than OSPF algorithm.

TDD-Based Rapid Fault Detection and Recovery for Fronthaul Bridged Network

Fronthaul bridged network has been a hot research topic in recent years. A challenge for existing path-control protocols for bridged networks is that the route computation is executed after the advertisement of the link fault based on the updated link state information. To address this issue, this letter proposes a novel fault detection and recovery scheme for fronthaul bridged networks. With the proposed scheme, bridges estimate the fault location utilizing the time-division duplex configuration of fronthaul streams and immediately switch the forwarding paths for the affected flows to backup paths. Consequently, the end-to-end communication of fronthaul streams is rapidly recovered compared with the existing protocols.

On the effectiveness of using network device state information for network path selection

Network path selection defines the methodology of selecting the best routes and forwarding traffic in a network service provider (NSP). NSPs use routing protocols that are optimized for a single arbitrary metric (i.e., administrative weight), which is commonly calculated according to the link state information, to select network paths. Despite the advantages, link-state protocols lack the ability to select network paths by considering the states of network devices such as the effect of routers for network path selection. Apparently, studying possible techniques for selecting network paths based on the state information of network devices, e.g., routers, has become obligatory. This paper studies the effectiveness of using network device state information for network path selection by presenting the Extended Server Link Router state Routing Protocol (ESLR). ESLR uses the state information of network devices as parameters to calculate a composite route metric; ESLR selects the network paths based on the cumulative propagation delay. By simulating ESLR using an ISP topology, the proposed protocol is examined for the effectiveness of using network device state information for network path selection.

Dynamic routing and spectrum assignment based on multilayer virtual topology and ant colony optimization in elastic software-defined optical networks

Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness.

An adaptive amoeba algorithm for shortest path tree computation in dynamic graphs

In today’s Internet, the shortest path tree (SPT) construction is an important issue in data exchange. To forward a data packet, each router uses routing protocols and link state information to identify the shortest paths from itself to other routers, which yields the shortest path tree. In reality, the network topology often varies over time. In existing studies, the locally affected nodes are identified and the shortest paths are recomputed so as to update the SPT. However, when the network size becomes large, the process of reconstructing the shortest paths for the affected nodes is very time consuming. Herein, we propose an adaptive amoeba method to build SPT in dynamic graphs. The proposed method is illustrated using a three-step procedure. First, we generalize the original Physarum model to enable it to have the ability to find the shortest paths in directed graphs. Secondly, the Physarum model is further extended to construct the shortest path tree when there are multiple sink nodes. Finally, we demonstrate that the developed method is capable of reconstructing the SPT by adapting the tube flow when link weight changes occur. Different from previous methods, the proposed algorithm is capable of identifying and recomputing the shortest paths for the affected nodes as well as maintaining the original paths for the unaffected vertices spontaneously. We demonstrate the performance of the proposed algorithm by comparing it with the Label Setting algorithm and Dijkstra algorithm in four randomly generated graphs. The computational results suggest the most appropriate algorithms to be used in different scenarios.

Trust aware cooperative optimised link state routing protocol

Optimised link state routing OLSR protocol is a popular proactive routing protocol for wireless ad-hoc networks WANETs. The functionality of OLSR protocol such as selection of multi-point relays, controlling the flooding of link state information and preparation of the routing table is depending upon the cooperation among the nodes in the network. However, open and decentralised network operations of WANETs introduce various security vulnerabilities to disrupt the routing process. To this end, this paper proposes a reputation-based trust model to enhance the cooperation among nodes in the routing process. The proposed trust model calculates the consolidate trust value CTV using direct and indirect observations. This CTV is used in the OLSR process to identify and isolate the misbehaving nodes from the routing path without having additional overhead. The simulation results show the effectiveness of the proposed method.