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From Algorithmic Computing to Autonomic Computing

In algorithmic computing, the program follows a predefined set of rules – the algorithm. The analyst/designer of the program analyzes the intended tasks of the program, defines the rules for its expected behaviour and programs the implementation. The creators of algorithmic software must therefore foresee, identify and implement all possible cases for its behaviour in the future application!

However, what if the problem is not fully defined? Or the environment is uncertain? What if situations are too complex to be predicted? Or the environment is changing dynamically? In many such cases algorithmic computing fails.
In such situations, the software needs an additional degree of freedom: Autonomy! Autonomy allows software to adapt to partially defined problems, to uncertain or dynamically changing environments and to situations that are too complex to be predicted. As more and more applications – such as autonomous cars and planes, adaptive power grid management, survivable networks, and many more – fall into this category, a gradual switch from algorithmic computing to autonomic computing takes place.

Autonomic computing has become an important software engineering discipline with a rich literature, an active research community, and a growing number of applications.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:14-qucosa-232820
Date13 February 2018
ContributorsTechnische Universität Dresden, Fakultät Informatik
PublisherSaechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageGerman, English
Detected LanguageEnglish
Typedoc-type:workingPaper
Formatapplication/pdf
Relationdcterms:isPartOf:Technische Berichte / Technische Universität Dresden, Fakultät Informatik; 2017,03 (TUD-FI17-03-Dezember 2017)

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