Preprocessing Monitoring Information on the SDN Data-Plane using P4

Hark, Rhaban; Bhat, Divyashri; Zink, Michael; Steinmetz, Ralf; Rizk, Amr

doi:10.1109/nfv-sdn47374.2019.9040030

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…Additionally, the complexity of features used in preprocessing algorithms can influence the computational and processing capacity of the switch. Therefore, it is important to manage the complexity to allow timely decision-making without overloading the switches [59]. For example, the current P4 switches only support a limited number of operations, which affects their ability to perform advanced data processing.…”

Section: Challenges and Future Of Ai-sdnmentioning

confidence: 99%

Exploring the Landscape of AI-SDN: A Comprehensive Bibliometric Analysis and Future Perspectives

Sahran,

Altarturi,

Anuar

2023

Electronics

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The rising influence of artificial intelligence (AI) enables widespread adoption of the technology in every aspect of computing, including Software-Defined Networking (SDN). Technological adoption leads to the convergence of AI and SDN, producing solutions that overcome limitations present in traditional networking architecture. Although numerous review articles discuss the convergence of these technologies, there is a lack of bibliometric trace in this field, which is important for identifying trends, new niches, and future directions. Therefore, this study aims to fill the gap by presenting a thorough bibliometric analysis of AI-related SDN studies, referred to as AI-SDN. The study begins by identifying 474 unique documents in the Web of Science (WoS) database published from 2009 until recently. The study uses bibliometric analysis to identify the general information, countries, authorship, and content of the selected articles, thereby providing insights into the geographical and institutional landscape shaping AI-SDN research. The findings provide a robust roadmap for further investigation in this field, including the background and taxonomy of the AI-SDN field. Finally, the article discusses several challenges and the future of AI-SDN in academic research.

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Section: Challenges and Future Of Ai-sdnmentioning

confidence: 99%

Exploring the Landscape of AI-SDN: A Comprehensive Bibliometric Analysis and Future Perspectives

Sahran,

Altarturi,

Anuar

2023

Electronics

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“…On contrary, telemetry systems that rely on programmable switches alone can scale to high traffic rates [28,29]. However, they can accommodate a limited set of telemetry items in order to achieve the scalability.…”

Section: Related Workmentioning

confidence: 99%

IntOpt: In-band Network Telemetry optimization framework to monitor network slices using P4

et al. 2022

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“…FlowStalker [18] subdivides a network into clusters and uses a special packet which collects monitoring information of the clusters. In [19], P4 switches preprocess monitoring information in order to forward requested statistics to an SDN controller. In FlowSpy [20], P4 switches proactively send monitoring information to a controller only when the counter of flow rules exceeds a threshold.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Although various monitoring methods [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] have been proposed to improve monitoring performance, existing monitoring mechanisms in SDN still have potential risks concerning monitoring overheads. Most of the previous monitoring methods are based on flow monitoring which introduces a heavy monitoring overhead in accordance with the increasing of the number of flow rules in SDN.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Scheme of Active-Port-Aware Monitoring in Software-Defined Networks

Vural

Wang

2021

IEEE Trans. Netw. Serv. Manage.

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Software-defined networking (SDN) is the key technology to enable network softwarization by offering a programable and flexible network control capabilities. In order to dynamically manage and reconfigure the underlying network through SDN, network-based monitoring functionality needs to be in place. However, existing network monitoring schemes are normally heavyweight which can cause substantial monitoring overhead when dealing with entire network infrastructure and complex policies. Such a limitation can be critical in a softwarebased network system that enables the construction of multiple networks with various network policies designed by a network operator. In this paper, we propose a new lightweight monitoring mechanism referred to as Active-port Aware Monitoring (APAM) in order to support the monitoring of complex networks with substantially reduced overhead. APAM typically monitors active ports which are the switch ports utilized by current flow rules. These active ports are dynamically monitored with reconfigurable monitoring intervals according to their port utilization. The measurement results show that APAM adapts varying traffic route due to a change of flow rules and also adjusts its monitoring performance according to network traffic dynamicity, which reduces the monitoring overhead and also improves monitoring accuracy.

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Preprocessing Monitoring Information on the SDN Data-Plane using P4

Cited by 7 publications

References 11 publications

Exploring the Landscape of AI-SDN: A Comprehensive Bibliometric Analysis and Future Perspectives

Exploring the Landscape of AI-SDN: A Comprehensive Bibliometric Analysis and Future Perspectives

IntOpt: In-band Network Telemetry optimization framework to monitor network slices using P4

A Lightweight Scheme of Active-Port-Aware Monitoring in Software-Defined Networks

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