Scalable Balanced Pipelined IPv6 Lookup Algorithm

One of the most critical router’s functions is the IP lookup. For each incoming IP packet, IP lookup determines the output port to which the packet should be forwarded. IPv6 addresses are envisioned to replace IPv4 addresses because the IPv4 address space is exhausted. Therefore, modern IP routers need to support IPv6 lookup. Most of the existing IP lookup algorithms are adjusted for the IPv4 lookup, but not for the IPv6 lookup. Scalability represents the main problem in the existing IP lookup algorithms because the IPv6 address space is much larger than the IPv4 address space due to longer IPv6 addresses. In this paper, we propose a novel IPv6 lookup algorithm that supports very large IPv6 lookup tables and achieves high IP lookup throughput.

Diffusion Model of a Non-Integer Order PIγ Controller with TCP/UDP Streams

In this article, a way to employ the diffusion approximation to model interplay between TCP and UDP flows is presented. In order to control traffic congestion, an environment of IP routers applying AQM (Active Queue Management) algorithms has been introduced. Furthermore, the impact of the fractional controller PIγ and its parameters on the transport protocols is investigated. The controller has been elaborated in accordance with the control theory. The TCP and UDP flows are transmitted simultaneously and are mutually independent. Only the TCP is controlled by the AQM algorithm. Our diffusion model allows a single TCP or UDP flow to start or end at any time, which distinguishes it from those previously described in the literature.

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

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.

Fluid flow approximation of time-limited TCP/UDP/XCP streams

This article presents the use of fluid flow approximation to model interactions between a set of TCP, UDP and XCP flows in the environment of IP routers using AQM (Active Queue Management) algorithms to control traffic congestion. In contrast to other works, independent UDP and TCP streams are considered and the model allows to start and end data transmissions in TCP, UDP and XCP streams at any time moment. It incorporates several Active Queue Management mechanisms: RED, NLRED, CHOKe.