Network bridge

Summary

A network bridge is a computer networking device that creates a single, aggregate network from multiple communication networks or network segments. This function is called network bridging. Bridging is distinct from routing. Routing allows multiple networks to communicate independently and yet remain separate, whereas bridging connects two separate networks as if they were a single network. In the OSI model, bridging is performed in the data link layer (layer 2). If one or more segments of the bridged network are wireless, the device is known as a wireless bridge. The main types of network bridging technologies are simple bridging, multiport bridging, and learning or transparent bridging. Transparent bridging Transparent bridging uses a table called the forwarding information base to control the forwarding of frames between network segments. The table starts empty and entries are added as the bridge receives frames. If a destination address entry is not found in the tabl

Official source

https://en.wikipedia.org/wiki/Network_bridge

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Related publications (2)

Verifying Software Network Functions with No Verification Expertise

George Candea, Luis David Figueiredo Mascarenhas Moreira Pedrosa, Rishabh Ramesh Iyer, Solal Vincenzo Pirelli, Matteo Rizzo

We present the design and implementation of Vigor, a software stack and toolchain for building and running software network middleboxes that are guaranteed to be correct, while preserving competitive performance and developer productivity. Developers write the core of the middlebox---the network function (NF)---in C, on top of a standard packet-processing framework, putting persistent state in data structures from Vigor's library; the Vigor toolchain then automatically verifies that the resulting software stack correctly implements a specification, which is written in Python. Vigor has three key features: network function developers need no verification expertise, and the verification process does not require their assistance (push-button verification); the entire software stack is verified, down to the hardware (full-stack verification); and verification can be done in a pay-as-you-go manner, i.e., instead of investing upfront a lot of time in writing and verifying a complete specification, one can specify one-off properties in a few lines of Python and verify them without concern for the rest. We developed five representative NFs---a NAT, a Maglev load balancer, a MAC-learning bridge, a firewall, and a traffic policer---and verified with Vigor that they satisfy standards-derived specifications, are memory-safe, and do not crash or hang. We show that they provide competitive performance. The Vigor framework is available at http://vigor.epfl.ch.

ACM2019

Performance Contracts for Software Network Functions

George Candea, Luis David Figueiredo Mascarenhas Moreira Pedrosa, Rishabh Ramesh Iyer, Solal Vincenzo Pirelli, Arseniy Zaostrovnykh

Software network functions (NFs), or middleboxes, promise flexibility and easy deployment of network services but face the serious challenge of unexpected performance behaviour. We propose the notion of a performance contract, a construct formulated in terms of performance critical variables, that provides a precise description of NF performance. Performance contracts enable fine-grained prediction and scrutiny of NF performance for arbitrary workloads, without having to run the NF itself. We describe BOLT, a technique and tool for computing such performance contracts for the entire software stack of NFs written in C, including the core NF logic, DPDK packet processing framework, and NIC driver. BOLT takes as input the NF implementation code and outputs the corresponding contract. Under the covers, it combines pre-analysis of a library of stateful NF data structures with automated symbolic execution of the NF’s code. We evaluate BOLT on four NFs—a Maglev-like load balancer, a NAT, an LPM router, and a MAC bridge—and show that its performance contracts predict the dynamic instruction count and memory access count with a maximum gap of 7% between the real execution and the conservatively predicted upper bound. With further engineering, this gap can be reduced.

USENIX ASSOC2019

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