Biologically-inspired design: getting it wrong and getting it right

Autores
White, Timothy
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Large, complex computing systems have many similarities to biological systems, at least at a high level. They consist of a very large number of components, the interactions between which are complex and dynamic, and the overall behavior of the system is not always predictable even if the components are well understood. These similarities have led the computing community to look to biology for design inspiration. But computing systems are not biological systems. Care must be taken when applying biological designs to computing systems, and we need to avoid applying them when they are not appropriate. We review three areas in which we have used biology as an inspiration to understand and construct computing systems. The first is the epidemiology of computer viruses, in which biological models are used to predict the speed and scope of global virus spread. The second is global defenses against computer viruses, in which the mammalian immune system is the starting point for design. The third is self-assembling autonomic systems, in which the components of a system connect locally, without global control, to provide a desired global function. In each area, we look at an approach that seems very biologically motivated, but that turns out to yield poor results. Then, we look at an approach that works well, and contrast it with the prior misstep. Perhaps unsurprisingly, attempting to reason by analogy is fraught with dangers. Rather, it is critical to have a detailed, rigorous understanding of the system being constructed and the technologies being used, and to understand the differences between the biological system and the computing system, as well as their similarities.
1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration: Just a dream?
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
computing systems
biological systems
sistema informático
ciencias biológicas
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23994

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network_name_str SEDICI (UNLP)
spelling Biologically-inspired design: getting it wrong and getting it rightWhite, TimothyCiencias Informáticascomputing systemsbiological systemssistema informáticociencias biológicasLarge, complex computing systems have many similarities to biological systems, at least at a high level. They consist of a very large number of components, the interactions between which are complex and dynamic, and the overall behavior of the system is not always predictable even if the components are well understood. These similarities have led the computing community to look to biology for design inspiration. But computing systems are not biological systems. Care must be taken when applying biological designs to computing systems, and we need to avoid applying them when they are not appropriate. We review three areas in which we have used biology as an inspiration to understand and construct computing systems. The first is the epidemiology of computer viruses, in which biological models are used to predict the speed and scope of global virus spread. The second is global defenses against computer viruses, in which the mammalian immune system is the starting point for design. The third is self-assembling autonomic systems, in which the components of a system connect locally, without global control, to provide a desired global function. In each area, we look at an approach that seems very biologically motivated, but that turns out to yield poor results. Then, we look at an approach that works well, and contrast it with the prior misstep. Perhaps unsurprisingly, attempting to reason by analogy is fraught with dangers. Rather, it is critical to have a detailed, rigorous understanding of the system being constructed and the technologies being used, and to understand the differences between the biological system and the computing system, as well as their similarities.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration: Just a dream?Red de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23994enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34632-5info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:37:13Zoai:sedici.unlp.edu.ar:10915/23994Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:37:14.184SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Biologically-inspired design: getting it wrong and getting it right
title Biologically-inspired design: getting it wrong and getting it right
spellingShingle Biologically-inspired design: getting it wrong and getting it right
White, Timothy
Ciencias Informáticas
computing systems
biological systems
sistema informático
ciencias biológicas
title_short Biologically-inspired design: getting it wrong and getting it right
title_full Biologically-inspired design: getting it wrong and getting it right
title_fullStr Biologically-inspired design: getting it wrong and getting it right
title_full_unstemmed Biologically-inspired design: getting it wrong and getting it right
title_sort Biologically-inspired design: getting it wrong and getting it right
dc.creator.none.fl_str_mv White, Timothy
author White, Timothy
author_facet White, Timothy
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
computing systems
biological systems
sistema informático
ciencias biológicas
topic Ciencias Informáticas
computing systems
biological systems
sistema informático
ciencias biológicas
dc.description.none.fl_txt_mv Large, complex computing systems have many similarities to biological systems, at least at a high level. They consist of a very large number of components, the interactions between which are complex and dynamic, and the overall behavior of the system is not always predictable even if the components are well understood. These similarities have led the computing community to look to biology for design inspiration. But computing systems are not biological systems. Care must be taken when applying biological designs to computing systems, and we need to avoid applying them when they are not appropriate. We review three areas in which we have used biology as an inspiration to understand and construct computing systems. The first is the epidemiology of computer viruses, in which biological models are used to predict the speed and scope of global virus spread. The second is global defenses against computer viruses, in which the mammalian immune system is the starting point for design. The third is self-assembling autonomic systems, in which the components of a system connect locally, without global control, to provide a desired global function. In each area, we look at an approach that seems very biologically motivated, but that turns out to yield poor results. Then, we look at an approach that works well, and contrast it with the prior misstep. Perhaps unsurprisingly, attempting to reason by analogy is fraught with dangers. Rather, it is critical to have a detailed, rigorous understanding of the system being constructed and the technologies being used, and to understand the differences between the biological system and the computing system, as well as their similarities.
1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration: Just a dream?
Red de Universidades con Carreras en Informática (RedUNCI)
description Large, complex computing systems have many similarities to biological systems, at least at a high level. They consist of a very large number of components, the interactions between which are complex and dynamic, and the overall behavior of the system is not always predictable even if the components are well understood. These similarities have led the computing community to look to biology for design inspiration. But computing systems are not biological systems. Care must be taken when applying biological designs to computing systems, and we need to avoid applying them when they are not appropriate. We review three areas in which we have used biology as an inspiration to understand and construct computing systems. The first is the epidemiology of computer viruses, in which biological models are used to predict the speed and scope of global virus spread. The second is global defenses against computer viruses, in which the mammalian immune system is the starting point for design. The third is self-assembling autonomic systems, in which the components of a system connect locally, without global control, to provide a desired global function. In each area, we look at an approach that seems very biologically motivated, but that turns out to yield poor results. Then, we look at an approach that works well, and contrast it with the prior misstep. Perhaps unsurprisingly, attempting to reason by analogy is fraught with dangers. Rather, it is critical to have a detailed, rigorous understanding of the system being constructed and the technologies being used, and to understand the differences between the biological system and the computing system, as well as their similarities.
publishDate 2006
dc.date.none.fl_str_mv 2006-08
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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