An overview of different techniques and algorithms for network topology discovery

Alhanani, Rafat Ahmed; Abouchabaka, Jâafar

doi:10.1109/icocs.2014.7061004

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…A survey paper by Ahmed et al 7 provided different techniques and algorithms for network topology discovery. The authors stated that most of the physical topology discovery algorithms are based on SNMP protocol.…”

Section: Related Workmentioning

confidence: 99%

A Network Topology Discovery Tool for Android Smart Phones

Saeed

Sheltami

Shakshuki

2015

Procedia Computer Science

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Android has invaded the majority of the smartphone market. Many developers in this domain are building up applications and addressing many problems. In this paper, we present Topology Discovery Algorithm (TDA) to discover nodes and the topology of the network for the Android Platform. The algorithm is based on Net Inventory System which is used to quickly discover the network topology without generating unnecessary traffic. To speed up the topology discovery process, we used multi-threading; thus, multiple threads run in parallel and handle all the network discovery queries.

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Section: Related Workmentioning

confidence: 99%

A Network Topology Discovery Tool for Android Smart Phones

Saeed

Sheltami

Shakshuki

2015

Procedia Computer Science

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“…The objective is to understand the dynamic of the engagement of patients with complex problems with the healthcare services. Therefore, the analogy of physical network topology [23] to highlight these dynamic features is used.…”

Section: Graph/network Modeling and Analysismentioning

confidence: 99%

Analyzing Patterns of Service Utilization Using Graph Topology to Understand the Dynamic of the Engagement of Patients with Complex Problems with Health Services

Bambi,

Santoso,

Moselle

et al. 2024

BioMedInformatics

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Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients’ engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients’ movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system.

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“…Network topology is the arrangement of the elements like links or nodes of a communication network, which is the complete overview of the whole network contains all the physical connections link among all network nodes [46,47,48].…”

Section: Network Topologymentioning

confidence: 99%

A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge

Islam¹,

Hossain²,

AlMukhtar³

2022

Preprint

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Software Defined Network (SDN) is the next generation network that decouples the control plane from the data plane of forwarding devices by utilizing the OpenFlow protocol as a communication link between the data plane and the control plane. However, there are some security issues might be in actions on SDN that the attackers can take control over the SDN control plane. Thus, traffic measurement is a fundamental technique of protecting SDN against the high-security threats such as DDoS, heavy hitter, superspreader as well as live video calling, QoS control, high bandwidth requirement, resource management are also inevitable in SDN/Software Defined Cellular Network (SDCN). In such a scenario, we survey SDN traffic measurement solutions, in order to assess how these solutions can make a secured, efficient and robust SDN/SDCN architecture. In this paper, various types of SDN traffic measurement solutions have been categorized based on network applications behaviour. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and future scope of research, which will guide to design and develop more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical and widely deployed SDN/SDCN in future prospects. More in details, we list out kinds of practical machine learning (ML) approaches to analyze how we can make improvement in the traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will give benefit to get secured SDN/SDCN network in complementary ways.

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An overview of different techniques and algorithms for network topology discovery

Cited by 7 publications

References 27 publications

A Network Topology Discovery Tool for Android Smart Phones

A Network Topology Discovery Tool for Android Smart Phones

Analyzing Patterns of Service Utilization Using Graph Topology to Understand the Dynamic of the Engagement of Patients with Complex Problems with Health Services

A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge

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