Network Topology Discovery Research Articles

Stop-Probability-Based Network Topology Discovery Method

Stop-Probability-Based Network Topology Discovery Method

Multipoint Relay Path for Efficient Topology Maintenance Algorithm in Optimized Link State Routing-Based for VANET

The Optimal Link State Routing (OLSR) protocol employs multipoint relay (MPR) nodes to disseminate topology control (TC) messages, enabling network topology discovery and maintenance. However, this approach increases control overhead and leads to wasted network bandwidth in stable topology scenarios due to fixed flooding periods. To address these challenges, this paper presents an Efficient Topology Maintenance Algorithm (ETM-OLSR) for Enhanced Link-State Routing Protocols. By reducing the number of MPR nodes, TC message generation and forwarding frequency are minimized. Furthermore, the algorithm selects a smaller subset of TC messages based on the changes in the MPR selection set from the previous cycle, adapting to stable and fluctuating network conditions. Additionally, the sending cycle of TC messages is dynamically adjusted in response to network topology changes. Simulation results demonstrate that the ETM-OLSR algorithm effectively reduces network control overhead, minimizes end-to-end delay, and improves network throughput compared to traditional OLSR and HTR-OLSR algorithms.

Design of an In-Band Control Plane for Automatic Bootstrapping and Fast Failure Recovery in P4 Networks

In this paper, we present the design of an in-band control plane system for P4 networks. The proposed in-band control plane performs automatic switch bootstrapping, network status monitoring, topology discovery and fast failover. The authentication and registration of newly connected switches are managed by the controller without manual intervention. The system can recover from any single link or single node failure in a short period of time. The recovery control paths for each single link and single node failure are prepared in advance. We propose an algorithm that takes recovery time into account for preplanning the recovery paths. In addition to handling any single failure, the proposed control plane is survivable even if there are multiple failures in the network. We implement an emulated network to evaluate the proposed control plane system. The experimental results show that the system successfully provides all the target functions.

GeoISA: A new road-topology-assisted geo-based content discovery scheme for vehicular named data networking

Content discovery represents a key foundation in realizing the promising vehicular named data networking (VNDN). The highly dynamic topology of VNDN prevents content discovery from becoming a straightforward task. In the VNDN literature, vehicle-to-vehicle content discovery mainly has flooding and time-contention-based (TCB) schemes. However, these schemes have transmission overhead issues or/and do not guarantee Interest forwarding in multiple directions. Thus, the likelihood of packet loss and retransmission is high, or/and the likelihood of locating content sources opportunities is low. Therefore, we proposed a new road-topology-assisted geo-based content discovery scheme for VNDN, termed GeoISA, to address the problems mentioned. In GeoISA, we first designed a new Interest forwarding suppression control mechanism named Interest suppression area (ISA). ISA demarcates an Interest forwarding suppression geo-area to determine the nodes' forwarding direction similarity and ensure forwarding Interest over different forwarding directions. Each road's pathway direction is considered a different Interest forwarding direction. The nodes' geo-position and traveling direction and the road's number of lanes and lane identifiers are involved in ISA demarcation. Second, an ISA-assisted distance-based TCB Interest forwarding strategy (GIDiS) was proposed to mitigate broadcast concerns. Finally, we conducted a simulation-based evaluation to compare GeoISA performance with the performance of four state-of-the-art solutions in an urban area scenario. Results demonstrate that GeoISA outperforms the other considered solutions regarding the Interest satisfaction ratio and the delay and path length (hops count) of the content delivery at the expense of a moderate transmission overhead. GeoISA can help ensure widespread Interest forwarding in many directions to boost the discovery of content sources and network topology in TCB schemes and intelligent context-aware forwarding solutions.

High-Speed Path Probing Method for Large-Scale Network.

In large-scale network topology discovery, due to the complex network structure and dynamic change characteristics, it is always the focus of network topology measurement to obtain as many network paths as possible in a short time. In this paper, we propose a large-scale network path probing approach in order to solve the problems of low probing efficiency and high probing redundancy commonly found in current research. By improving the packet delivery order and the update strategy of time-to-live field values, we redesigned and implemented an efficient large-scale network path probing tool. The experimental results show that the method-derived tool can complete path probing for a sample of 12 million/24 network address segments worldwide within 1 hour, which greatly improves the efficiency of network path probing. Meanwhile, compared to existing methods, the proposed method can reduce the number of packets sent by about 10% with the same number of network addresses found, which effectively reduces probing redundancy and alleviates the network load.

Internet of Things Network Topology Discovery Algorithm Based on Wireless Sensors

Remote sensor network is a multijump self-coordinating organization framework shaped by countless energy-restricted miniature sensor hubs sent in the observing region. Be that as it may, the correspondence conventions of customary organization configuration cannot be straightforwardly applied to remote sensor organizations. The improvement of committed correspondence conventions and estimation techniques has turned into a pressing exploration subject in the field of remote sensor organizations. The research purpose of this paper is to research the Internet of Things network topology discovery algorithm based on wireless sensors. This paper analyzes and mines an adaptive neighbor discovery scheduling algorithm with lower latency, acquires the information of potential neighbor nodes based on existing neighbor nodes, discovers potential neighbor nodes by proactively waking up, and studies the information recommendation mechanism between neighbor nodes and compares. The intimacy between neighbor nodes (such as common neighbor rate) is used to selectively receive recommended information from neighbor nodes, thereby filtering redundant data information, lessening hub energy utilization, and accomplishing the motivation behind broadening the organization life cycle. A geography disclosure calculation in light of versatile specialists is proposed to tackle the geography revelation issue under the various leveled geography structure in remote sensor organizations. The calculation joins the qualities of portable specialist innovation and various leveled Internet of Things network geography and simultaneously further develops the relocation methodology of versatile specialists for information assortment, intermittent undertaking assignment, and organization status observing. Research shows that in the investigation of the effect of hub obligation cycle on framework execution, the energy utilization of the DDC-Group calculation is marginally expanded contrasted and the gathering calculation, and the increase is not more than 1%, but compared to the CNR-Group increase more.

Models and Methods for Discovery of Local Area Network Topology with Incomplete Data

A lot of network management tasks require a description of the logical and physical computer network topology. Obtaining such a description in an automatic way is complicated due to the possibility of incompleteness and incorrectness of the initial data on the network structure. This article provides a study on the properties of incomplete initial data on network device connectivity on the link layer. Methods for generalized handling of the heterogeneous input data on the link layer are included. We describe models and methods for deriving a missing part of the data, as well as the condition in which it is possible to get a single correct network topology description. The article includes algorithms for building a link layer topology description from incomplete data when this data is possible to fulfill up to the required level. Also, we provide methods for detecting and resolving an ambiguity in the data and methods for improving incorrect initial data. The tests and evaluations provided in the article demonstrate the applicability and effectiveness of the build methods for discovering various heterogeneous real-life networks. Additionally, we show the advantages of the provided methods over the previous analogs: our methods are able to derive up to 99% data on link layer connectivity in polynomial time; able to provide a correct solution from an ambiguous data.

Models and methods for local area network topology discovery with incomplete data

A lot of network management tasks require a description of the logical and physical computer network topology. Obtaining such a description in an automatic way is complicated due to the possibility of incompleteness and incorrectness of the initial data on the network structure. This article provides a study on the properties of incomplete initial data on network device connectivity on the link layer. Methods for generalized handling of the heterogeneous input data on the link layer are included. We describe models and methods for deriving a missing part of the data, as well as the condition in which it is possible to get a single correct network topology description. The article includes algorithms for building a link layer topology description from incomplete data when this data is possible to fulfill up to the required level. Also, we provide methods for detecting and resolving an ambiguity in the data and methods for improving incorrect initial data. Tests and evaluations provided in the article demonstrate the applicability and effectiveness of the build methods for discovering various heterogeneous real-life networks. Additionally, we show advantages of the provided methods over the previous analogs: our methods are able to derive up to 99\% data on link layer connectivity in polynomial time; able to provide a correct solution from an ambiguous data.

Efficient Topology Discovery for Software-Defined Networks

OpenFlow discovery protocol (OFDP) is the standard protocol for discovering network topologies for OpenFlow. Each controller generates and receives OFDP messages for each switch to yield a network topology. However, for a network with a large number of switches, the controller may suffer from high overhead to perform OFDP even though the network topology remains unchanged. In this article, we improve the efficiency of network topology discovery by implementing a delegated function in OpenFlow switches. Our mechanism can reduce controller’s overhead for topology discovery to shorten the time of discovering a network topology. It also provides with the interoperability with switches which only perform OFDP. The experimental results demonstrate that the proposed scheme can improve the time for discovering a new network topology by decreasing the number of messages. Our scheme also eliminates unnecessary Packet-Out and Packet-In messages when the network topology remains unchanged. As a result, the controller overhead, including CPU usage and traffic load, is greatly reduced to shorten the response latency of controllers. Hence, the proposed scheme improves the feasibility of achieving scalable OpenFlow networking.

An Efficient Topology Discovery Protocol with Node ID Assignment Based on Layered Model for Underwater Acoustic Networks.

Underwater acoustic networks are widely used in survey missions and environmental monitoring. When an underwater acoustic network (UAN) is deployed in a marine region or two UANs merge, each node hardly knows the entire network and may not have a unique node ID. Therefore, a network topology discovery protocol that can complete node discovery, link discovery, and node ID assignment are necessary and important. Considering the limited node energy and long propagation delay in UANs, it is challenging to obtain the network topology with reduced overheads and a short delay in this initial network state. In this paper, an efficient topology discovery protocol (ETDP) is proposed to achieve adaptive node ID assignment and topology discovery simultaneously. To avoiding packet collision in this initial network state, ETDP controls the transmission of topology discovery (TD) packets, based on a local timer, and divides the network into different layers to make nodes transmit TD packets orderly. Exploiting the received TD packets, each node could obtain the network topology and assign its node ID independently. Simulation results show that ETDP completes network topology discovery for all nodes in the network with significantly reduced energy consumption and short delay; meanwhile, it assigns the shortest unique IDs to all nodes with reduced overheads.

Benchmarking of mainstream SDN controllers over open off‐the‐shelf software‐switches

The decoupling of the data plane and the control plane that became real through the advent of the Software‐Defined Networking (SDN) paradigm, has made possible the fast and flexible customized prototyping of testbed environments. One of the critical challenges that must be faced is the deployment of low‐cost high‐performance open testbeds. Our group built a low‐cost testbed that is highly flexible and customizable, and able to leverage a set of Key Performance Indicators (KPIs) for the minimal combination of components needed to achieve more efficient outcomes in specific scenarios. In light of this, we assessed the use of ONOS, Floodlight, and Ryu mainstream SDN controllers, on top of low‐cost open routers/switches platforms in which both OpenWrt and LEDE operating systems are embedded, to provide in‐depth discussions on different aspects of the subject. When measured in terms of (a) throughput and (b) network topology discovery time, the results of the assessment provided unpublished performance and scalability perspectives about the relationship between the controller and the operating system.

Applying OSiRIS NMAL to Network Slices on SLATE

The Network Management Abstraction Layer (NMAL) extends perfSONAR capabilities to include automated network topology discovery and tracking in the Unified Network Information Service (UNIS), and incorporate Software Defined Networking (SDN) into overall operations of the OSiRIS distributed Ceph infrastructure. We deploy perfSONAR components both within OSiRIS and at our “client” locations to allow monitoring and measuring the networks interconnecting science domain users and OSiRIS components. Topology discovery (using an SDN controller application) and Flange Network Orchestration (NOS) rules are used to dynamically manage network pathing in our testbed environments. NMAL components have been containerized to operate within the Services Layer at the Edge (SLATE) infrastructure, and we describe our experiences in packaging and deploying our services.

Improvement and implementation of Wireless Network Topology System based on SNMP protocol for router equipment

With the continuous expansion of computer network scale and the diversity of network equipment, the traditional single computer network maintenance and management method is no longer applicable. Therefore, in order to achieve efficient and accurate management of computer networks and ensure the stable operation of computer networks in various applications, computer network management software with superior performance and corresponding topology discovery technology become the key. In this paper, SNMP (Simple Network Management Protocol) network topology discovery algorithm is deeply analyzed in the laboratory computer network environment, and its shortcomings are pointed out. In view of the shortcomings of traditional SNMP algorithm, this algorithm is optimized and improved, and helps ICMP (Internet Control Message Protocol) detect the internal subnet, so as to discover the main network topology accurately and efficiently. In order to solve the heterogeneity of information storage modes in MIB (Management Information Base) libraries of different devices in laboratory network environment, this paper innovatively adds an optimized SNMP algorithm aiming at the heterogeneity of network devices, thus realizing the universality of network topology. Finally, the experiments show that the time dissipation of the proposed algorithm is less than 10 data points compared with the traditional algorithm under the same experimental conditions. The laboratory network environment based on the proposed system can achieve a fast and smooth laboratory network, which has obvious advantages compared with the traditional network management system.

A microscopic-view Infection model based on linear systems

Understanding the behavior of an infection network is typically addressed from either a microscopic or a macroscopic point-of-view. The trade-off is between following the individual states at some added complexity cost or looking at the ratio of infected nodes. In this paper, we focus on developing an alternative approach based on dynamical linear systems that combines the fine information of the microscopic view without the associated added complexity. Attention is shifted towards the problems of source localization and network topology discovery in the context of infection networks where a subset of the nodes is elected as observers. Finally, the possibility to control such networks is also investigated. Simulations illustrate the conclusions of the paper with particular interest on the relationship of the aforementioned problems with the topology of the network and the selected observer/controller nodes.

An Optimization Algorithm of Network Topology Discovery Based on SNMP Protocol

Through the analysis of network topology discovery algorithm used ICMP protocol and FDB address, a novel layer topology discovery and link layer topology discovery algorithm which is suitable for campus network environment is proposed based on SNMP protocol. This algorithm can rapidly and accurately calculate the second and third floors topology of the whole pipe network.

IoT환경에서의 부하 균형을 이룬 네트워크 토폴로지 탐색

IoT환경에서의 부하 균형을 이룬 네트워크 토폴로지 탐색

SOUNET: Self-Organized Underwater Wireless Sensor Network.

In this paper, we propose an underwater wireless sensor network (UWSN) named SOUNET where sensor nodes form and maintain a tree-topological network for data gathering in a self-organized manner. After network topology discovery via packet flooding, the sensor nodes consistently update their parent node to ensure the best connectivity by referring to the time-varying neighbor tables. Such a persistent and self-adaptive method leads to high network connectivity without any centralized control, even when sensor nodes are added or unexpectedly lost. Furthermore, malfunctions that frequently happen in self-organized networks such as node isolation and closed loop are resolved in a simple way. Simulation results show that SOUNET outperforms other conventional schemes in terms of network connectivity, packet delivery ratio (PDR), and energy consumption throughout the network. In addition, we performed an experiment at the Gyeongcheon Lake in Korea using commercial underwater modems to verify that SOUNET works well in a real environment.

Discovering the Network Topology: An Efficient Approach for SDN

Network topology is a physical description of the overall resources in the network. Collecting this information using efficient mechanisms becomes a critical task for important network functions such as routing, network management, quality of service (QoS), among many others. Recent technologies like Software-Defined Networks (SDN) have emerged as promising approaches for managing the next generation networks. In order to ensure a proficient topology discovery service in SDN, we propose a simple agents-based mechanism. This mechanism improves the overall efficiency of the topology discovery process. In this paper, an algorithm for a novel Topology Discovery Protocol (SD-TDP) is described. This protocol will be implemented in each switch through a software agent. Thus, this approach will provide a distributed solution to solve the problem of network topology discovery in a more simple and efficient way.

Design and Implementation of a Stateful PCE-Based Unified Control and Management Framework for Carrier-Grade MPLS-TP Networks

This paper presents the design and evaluation of a generalized topology discovery, operational monitoring, and provisioning (G-TOP) controller for a carrier-grade transport network. The G-TOP controller is a vendor-specific extension of the stateful PCE that integrates network topology discovery, path computation, and provisioning functions, but decouples the switch specific management framework. The G-TOP protocol enables the G-TOP controller to collect topology information, set up LSPs, and monitor state both from and to the management framework in a unified way. To validate the feasibility of the proposed framework, the author implemented a testbed using a multi-protocol label switching transport profile (MPLS-TP) switches and performed experimental testing. The experimental results show that the centralized control framework reduces provisioning time by approximately 68 ms and the autonomous management framework reduces fault recovery time by approximately 27 ms, while reducing the traffic load on the controller by several orders of magnitude compared to the OpenFlow approach.

Event Driven Network Topology Discovery and Inventory Listing Using REAMS

Network Topology Discovery and Inventory Listing are two of the primary features of modern network monitoring systems (NMS). Current NMSs rely heavily on active scanning techniques for discovering and mapping network information. Although this approach works, it introduces some major drawbacks such as the performance impact it can exact, specially in larger network environments. As a consequence, scans are often run less frequently which can result in stale information being presented and used by the network monitoring system. Alternatively, some NMSs rely on their agents being deployed on the hosts they monitor. In this article, we present a new approach to Network Topology Discovery and Network Inventory Listing using only passive monitoring and scanning techniques. The proposed techniques rely solely on the event logs produced by the hosts and network devices present within a network. Finally, we discuss some of the advantages and disadvantages of our approach.