Content based discarding in IP-routers

Rough Set Theory for Optimization of Packet Management Mechanism in IP Routers

Advances in Intelligent Systems and Computing - Web, Artificial Intelligence and Network Applications ◽

10.1007/978-3-030-44038-1_119 ◽

2020 ◽

pp. 1308-1320

Author(s):

Jolanta Mizera-Pietraszko ◽

Jolanta Tancula ◽

Ireneusz Jozwiak

Keyword(s):

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Management Mechanism ◽

Ip Routers

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An optical interconnection network for terabit IP routers

Journal of Lightwave Technology ◽

10.1109/50.908820 ◽

2000 ◽

Vol 18 (12) ◽

pp. 2095-2112 ◽

Cited By ~ 8

Author(s):

H.J. Chao ◽

Ti-Shiang Wang

Keyword(s):

Interconnection Network ◽

Optical Interconnection ◽

Ip Routers

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A Fuzzy Logic Based Network Congestion Control Using Active Queue Management Techniques

Journal of Scientific Research ◽

10.3329/jsr.v2i2.2786 ◽

2010 ◽

Vol 2 (2) ◽

pp. 273-284 ◽

Cited By ~ 4

Author(s):

I. K. Tabash ◽

M. A. Mamun ◽

A. Negi

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Probability Function ◽

Active Queue Management ◽

Buffer Size ◽

Queue Management ◽

Passive Devices ◽

Better Regulation ◽

Ip Routers ◽

Intelligent Approach

Conventional IP routers are passive devices that accept packets and perform the routing function on any input. Usually the tail-drop (TD) strategy is used where the input which exceeds the buffer capacity are simply dropped. In active queue management (AQM) methods routers manage their buffers by dropping packets selectively. We study one of the AQM methods called as random exponential marking (REM). We propose an intelligent approach to AQM based on fuzzy logic controller (FLC) to drop packets dynamically, keep the buffer size around desired level and also prevent buffer overflow. Our proposed approach is based on REM algorithm, which drops the packets by drop probability function. In our proposal we replace the drop probability function by a FLC to drop the packets, stabilize the buffer around the desired size and reduce delay. Simulation results show a better regulation of the buffer. Keywords: Random exponential marking; Active queue management; Fuzzy logic controller; Pro-active queue management. © 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v2i2.2786 J. Sci. Res. 2 (2), 273-284 (2010)

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Empirical Prediction of Computer-Network Evolution

Selected Readings on Telecommunications and Networking ◽

10.4018/978-1-60566-094-3.ch014 ◽

2011 ◽

pp. 198-213

Author(s):

Sami J. Habib

Keyword(s):

Computer Network ◽

Network Evolution ◽

Network Technology ◽

Empirical Prediction ◽

Technology Changes ◽

Ip Routers ◽

And Performance ◽

The Cost ◽

Network Technologies ◽

Design Cost

This article presents a computer-aided integration tool, iCAD, that can predict a network evolution. We have used the term a network evolution to mean predicting changes within the physical network topology as time evolves. iCAD is connected to four device libraries, each of which contains a distinct set of network-technology devices, such as Ethernet hubs, ATM switches, IP routers, and gateways. As a network technology changes, each device library is updated. Then, we have plotted the cost and performance changes between the old and recent network technologies, enabling us to predict future changes to a first order. This article presents empirical results from 1999 until 2005 recording the network evolution progress, where the lower and upper bounds of network evolution came out to be 10% to 25% and 57% to 74% respectively in terms of network-design cost reduction.

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Packet Capture and Analysis on MEDINA, A Massively Distributed Network Data Caching Platform

Parallel Processing Letters ◽

10.1142/s0129626417500104 ◽

2017 ◽

Vol 27 (03n04) ◽

pp. 1750010 ◽

Cited By ~ 1

Author(s):

Amedeo Sapio ◽

Mario Baldi ◽

Fulvio Risso ◽

Narendra Anand ◽

Antonio Nucci

Keyword(s):

Fog Computing ◽

Observation Point ◽

Packet Capture ◽

Distributed Index ◽

Efficient Retrieval ◽

Distributed Execution ◽

Generic Tasks ◽

Ip Routers ◽

Processing And Storage ◽

And Storage

Traffic capture and analysis is key to many domains including network management, security and network forensics. Traditionally, it is performed by a dedicated device accessing traffic at a specific point within the network through a link tap or a port of a node mirroring packets. This approach is problematic because the dedicated device must be equipped with a large amount of computation and storage resources to store and analyze packets. Alternatively, in order to achieve scalability, analysis can be performed by a cluster of hosts. However, this is normally located at a remote location with respect to the observation point, hence requiring to move across the network a large volume of captured traffic. To address this problem, this paper presents an algorithm to distribute the task of capturing, processing and storing packets traversing a network across multiple packet forwarding nodes (e.g., IP routers). Essentially, our solution allows individual nodes on the path of a flow to operate on subsets of packets of that flow in a completely distributed and decentralized manner. The algorithm ensures that each packet is processed by n nodes, where n can be set to 1 to minimize overhead or to a higher value to achieve redundancy. Nodes create a distributed index that enables efficient retrieval of packets they store (e.g., for forensics applications). Finally, the basic principles of the presented solution can also be applied, with minimal changes, to the distributed execution of generic tasks on data flowing through a network of nodes with processing and storage capabilities. This has applications in various fields ranging from Fog Computing, to microservice architectures and the Internet of Things.

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