How Powerful Switches Should be Deployed: A Precise Estimation Based on Queuing Theory

Shen, Gengbiao; Li, Qing; Ai, Shuo; Jiang, Yong; Xu, Mingwei; Jia, Xuya

doi:10.1109/infocom.2019.8737629

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…As part of the presented scientific research, it is reasonable to determine aspects of the queuing theory studied in previous research papers. In the scientific work of Shen et al [1] proposed the authors' solution to the problem of evaluating the space of flow tables on the basis of queuing theory. The authors' unique contribution is to create a probabilistic model of routing strategies to quantize the impact of path selection, which in our opinion is an interesting solution within the framework of queuing theory.…”

Section: Literature Review and Defining The Problemmentioning

confidence: 99%

Generalising cases of insensitive queuing city traffic management systems and their impact on the delivery of goods by air and road

Kuchma,

Gabrielova,

Lytvynenko

et al. 2024

MATEC Web Conf.

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It is established that the urban traffic management system, as well as the aviation traffic management system, has a significant impact on the multimodal delivery of goods in the road-air transport, which requires the creation of tools for theoretical substantiation and practical implementation. The authors� method of determination the occurrence of vehicles queues in cities using mathematical modelling tools for three scenarios of crossing intersections was developed. The conceptual scheme for calculating the occurrence of the queue of vehicles was proposed. It was noted that vehicles in the queue often arrive randomly, the duration of service is constant, and the probability that before the queue disappears for the first time a certain number of cars cross the intersection has a Borel-Tanner distribution. The given mathematical modelling should become a part of tools for the implementation of the integrated intelligent information system of cities as a cluster of the city traffic management system.

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Section: Literature Review and Defining The Problemmentioning

confidence: 99%

Generalising cases of insensitive queuing city traffic management systems and their impact on the delivery of goods by air and road

Kuchma,

Gabrielova,

Lytvynenko

et al. 2024

MATEC Web Conf.

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“…Despite many technological advances, the quantity of simultaneous network traffic flows still overwhelms the capacity of flow tables. 81 Moreover, controller throughput can impose additional limits on the rate of incoming flows in SDN centralized control planes. 82 Addressing these issues requires HH flow identification, while an emphasis should be put on early flow classification.…”

Section: Related Workmentioning

confidence: 99%

“…UNIV1 has been used since its publication in several applications as a ground truth. 81,85,[100][101][102][103] UNIV1 was collected in a university campus data center that serves administrative staff and students. The provided services range from system backups and hosting distributed file systems, through e-mail servers and Web services, to multicast video streams.…”

Section: Univ1 Traffic Tracementioning

confidence: 99%

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Towards threshold‐agnostic heavy‐hitter classification

et al. 2021

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A heavy-hitter (HH) network traffic flow consumes considerably more network resources than other flows combined. The classification of HHs is critical to provide, among others, the required level of Quality of Service and reliability in both conventional and data center networks. HH classification is typically threshold-based. However, there is no consistent and accepted threshold or set of thresholds that would reliably classify flows. Furthermore, existing threshold-driven approaches use counters (e.g., duration, packets, and bytes); thus, their accuracy depends on how complete the flow information is. This paper paves the way to threshold-agnostic HH identification by proposing an approach that performs HH classification based on per-flow packet size distribution (PSD) and template matching (TM). PSD allows capturing the behavior and dynamism of network traffic flows (even from their first few packets). TM enables to classify HHs by measuring the similarity between the PSD of observed flows and a set of master templates representing the flow size behavior of HH classes. We evaluated the PSD-and TM-based approach using flows extracted from real traffic traces. Results show that our approach classifies HHs accurately and timely, corroborating that the threshold-less perspective is feasible for HH identification.

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“…The challenge with flow-based traffic engineering is that the number of simultaneous flows in a network often surpasses the capacity of flow tables in switches [4]. Additionally, in centralized software-defined networks, the controller's throughput for managing new flows is limited.…”

Section: Introductionmentioning

confidence: 99%

Elephant Flow Classification on the First Packet With Neural Networks

Kądziołka,

Jurkiewicz,

Wójcik

et al. 2024

IEEE Access

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Quick and accurate identification of the largest flows in the network would allow for the management of most traffic using dedicated, flow-specific routes and policies, thereby significantly reducing the overall number of entries in switch flow tables. Our analysis focuses on utilizing neural networks to classify elephant flows based on the first packet using 5-tuple packet header fields. The findings indicate that with simple neural networks comprising solely linear layers, it is possible to accurately detect elephant flows at their inception, thereby reducing the number of flow table entries -by up to a factor of 15 -while still effectively covering 80% of the network traffic with individual flow entries.

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How Powerful Switches Should be Deployed: A Precise Estimation Based on Queuing Theory

Cited by 13 publications

References 24 publications

Generalising cases of insensitive queuing city traffic management systems and their impact on the delivery of goods by air and road

Generalising cases of insensitive queuing city traffic management systems and their impact on the delivery of goods by air and road

Towards threshold‐agnostic heavy‐hitter classification

Elephant Flow Classification on the First Packet With Neural Networks

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