System and architecture evaluation framework using cross-domain dynamic complexity measures

Jonathan Fischi, Roshanak Nilchiani, Jon Wade

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work proposes a framework for effective quantification and comparison of system complexity content when architecting systems. Properly interpreted results from complexity analysis help make better informed system architecture selection between competing designs since the increased complexity of a system can lead to increased fragility and more exposure to failures and risks. Therefore the quantification of complexity is important when designing and planning the operation of a complex system. Our prior work on dynamic complexity measures provides the foundation for the framework discussed herein. The scope of this paper is to apply dynamic complexity measures to current, real-world complex systems. This work introduces a multi-step framework to evaluate complex systems and enhance a systems engineer's ability to compare competing systems/architectures. The framework has also proved useful for generating technical risks. A case study is included which utilizes the framework to evaluate an autonomous car architecture being developed by Google. The results demonstrate how the framework can help guide stakeholder decisions. The findings advance system complexity evaluation state-of-the-art by providing a framework using behavioral-based dynamic complexity measures.

Original languageEnglish
Title of host publication10th Annual International Systems Conference, SysCon 2016 - Proceedings
ISBN (Electronic)9781467395182
DOIs
StatePublished - 13 Jun 2016
Event10th Annual International Systems Conference, SysCon 2016 - Orlando, United States
Duration: 18 Apr 201621 Apr 2016

Publication series

Name10th Annual International Systems Conference, SysCon 2016 - Proceedings

Conference

Conference10th Annual International Systems Conference, SysCon 2016
Country/TerritoryUnited States
CityOrlando
Period18/04/1621/04/16

Keywords

  • Complexity theory
  • Measurement techniques
  • System-level design
  • Systems engineering and theory

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