Publication

Is the Network Turing-Complete?

2013
Reports

Abstract

Ensuring correct network behavior is hard. This is the case even for simple networks, and adding middleboxes only complicates this task. In this paper, we demonstrate a fundamental property of networks. Namely, we show a way of using a network to emulate the Rule 110 cellular automaton. We do so using just a set of network devices with simple features such as packet matching, header rewriting and round-robin loadbalancing. Therefore, we show that a network can emulate any Turing machine. This ultimately means that analyzing dynamic network behavior can be as hard as analyzing an arbitrary program. Analyzing a network containing middleboxes is already understood to be hard. Our result shows that using even only statically configured switches can make the problem intractable.

Official source

https://infoscience.epfl.ch/entities/publication/6e74f95a-c04a-491c-9a95-83414762a356

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2013
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/6e74f95a-c04a-491c-9a95-83414762a356

About this result

Related concepts (32)

Network theory

In mathematics, computer science and network science, network theory is a part of graph theory. It defines networks as graphs where the nodes or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components. Network theory has applications in many disciplines, including statistical physics, particle physics, computer science, electrical engineering, biology, archaeology, linguistics, economics, finance, operations research, climatology, ecology, public health, sociology, psychology, and neuroscience.

Computer network

A computer network is a set of computers sharing resources located on or provided by network nodes. Computers use common communication protocols over digital interconnections to communicate with each other. These interconnections are made up of telecommunication network technologies based on physically wired, optical, and wireless radio-frequency methods that may be arranged in a variety of network topologies. The nodes of a computer network can include personal computers, servers, networking hardware, or other specialized or general-purpose hosts.

Network topology

Network topology is the arrangement of the elements (links, nodes, etc.) of a communication network. Network topology can be used to define or describe the arrangement of various types of telecommunication networks, including command and control radio networks, industrial fieldbusses and computer networks. Network topology is the topological structure of a network and may be depicted physically or logically. It is an application of graph theory wherein communicating devices are modeled as nodes and the connections between the devices are modeled as links or lines between the nodes.

Related publications (32)

Is the Network Turing-Complete?

Chat with Graph Search

Small-world scale-free brain networks from EEG with application to motor imagery decoding and brain fingerprinting

A Unified Framework for Modelling Modern Relational Network Data

MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation