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Defining Emergent Software Using Continuous Self-Assembly, Perception, and Learning

Defining Emergent Software Using Continuous Self-Assembly, Perception, and Learning Architectural self-organisation, in which different configurations of software modules are dynamically assembled based on the current context, has been shown to be an effective way for software to self-optimise over time. Current approaches to this rely heavily on human-led definitions: models, policies, and processes to control how self-organisation works. We present the case for a paradigm shift to fully emergent computer software that places the burden of understanding entirely into the hands of software itself. These systems are autonomously assembled at runtime from discovered constituent parts and their internal health and external deployment environment continually monitored. An online, unsupervised learning system then uses runtime adaptation to continuously explore alternative system assemblies and locate optimal solutions. Based on our experience over the past 3 years, we define the problem space of emergent software and present a working case study of an emergent web server as a concrete example of the paradigm. Our results demonstrate two main aspects of the problem space for this case study: that different assemblies of behaviour are optimal in different deployment environment conditions and that these assemblies can be autonomously learned from generalised perception data while the system is online. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

Defining Emergent Software Using Continuous Self-Assembly, Perception, and Learning

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 ACM
ISSN
1556-4665
eISSN
1556-4703
DOI
10.1145/3092691
Publisher site
See Article on Publisher Site

Abstract

Architectural self-organisation, in which different configurations of software modules are dynamically assembled based on the current context, has been shown to be an effective way for software to self-optimise over time. Current approaches to this rely heavily on human-led definitions: models, policies, and processes to control how self-organisation works. We present the case for a paradigm shift to fully emergent computer software that places the burden of understanding entirely into the hands of software itself. These systems are autonomously assembled at runtime from discovered constituent parts and their internal health and external deployment environment continually monitored. An online, unsupervised learning system then uses runtime adaptation to continuously explore alternative system assemblies and locate optimal solutions. Based on our experience over the past 3 years, we define the problem space of emergent software and present a working case study of an emergent web server as a concrete example of the paradigm. Our results demonstrate two main aspects of the problem space for this case study: that different assemblies of behaviour are optimal in different deployment environment conditions and that these assemblies can be autonomously learned from generalised perception data while the system is online.

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery

Published: Sep 20, 2017

Keywords: Emergent software

References

  • A world full of surprises: Bayesian theory of surprise to quantify degrees of uncertainty
    Bencomo, Nelly; Belaggoun, Amel
  • Dynamic decision networks for decision-making in self-adaptive systems: A case study
    Bencomo, Nelly; Belaggoun, Amel; Issarny, Valerie
  • A role-based imitation algorithm for the optimisation in dynamic fitness landscapes
    Cakar, Emre; Tomforde, Sven; Müller-Schloer, Christian
  • Self-adaptation through incremental generative model transformations at runtime
    Chen, Bihuan; Peng, Xin; Yu, Yijun; Nuseibeh, Bashar; Zhao, Wenyun
  • FUSION: A framework for engineering self-tuning self-adaptive software systems
    Elkhodary, Ahmed; Esfahani, Naeem; Malek, Sam
  • A meta-controller method for improving run-time self-architecting in SOA systems
    Ewing, John M.; Menascé, Daniel A.
  • Architecting self-aware software systems
    Faniyi, Funmilade; Lewis, Peter R.; Bahsoon, Rami; Yao, Xin
  • Multi-agent Systems: An Introduction to Distributed Artificial Intelligence
    Ferber, Jacques
  • Source code from this article with instructions
    Filho, Roberto Rodrigues
  • Quantitative emergence—A refined approach based on divergence measures
    Fisch, Dominik; Janicke, Martin; Sick, Bernhard; Muller-Schloer, Christian
  • Experiments in Emergent Programming Using Self-organizing Multi-agent Systems
    Georgé, Jean-Pierre; Gleizes, Marie-Pierre
  • Online feature selection for pixel classification
    Glocer, Karen; Eads, Damian; Theiler, James
  • Experiences with open overlays: A middleware approach to network heterogeneity
    Grace, Paul; Hughes, Danny; Porter, Barry; Blair, Gordon; Coulson, Geoff; Taiani, Francois
  • Minimizing nasty surprises with better informed decision-making in self-adaptive systems
    Hassan, Sara; Bencomo, Nelly; Bahsoon, Rami
  • Building dynamic software product lines
    Hinchey, Mike; Park, Sooyong; Schmid, Klaus
  • Adapting component-based systems at runtime via policies with temporal patterns
    Kouchnarenko, Olga; Weber, Jean-Francois
  • SASSY: A framework for self-architecting service-oriented systems
    Menasce, D.; Gomaa, H.; Malek, S.; Sousa, J. P.
  • NASA web server trace
    NASA.,
  • Runtime modularity in complex structures: A component model for fine grained runtime adaptation
    Porter, Barry
  • REX: A development platform and online learning approach for runtime emergent software systems
    Porter, Barry; Grieves, Matthew; Filho, Roberto Rodrigues; Leslie, David
  • Losing control: The case for emergent software systems using autonomous assembly, perception and learning
    Porter, Barry; Filho, Roberto Rodrigues
  • Self-adaptive software: Landscape and research challenges
    Salehie, Mazeiar; Tahvildari, Ladan
  • Reinforcement Learning: An Introduction
    Sutton, Richard S.; Barto, Andrew G.
  • Incremental design of organic computing systems—moving system design from design-time to runtime
    Tomforde, Sven; Hähner, Jörg; Müller-Schloer, Christian
  • Self-repair through reconfiguration: A requirements engineering approach
    Wang, Yiqiao; Mylopoulos, John
  • MassConf: Automatic configuration tuning by leveraging user community information
    Zheng, Wei; Bianchini, Ricardo; Nguyen, Thu D.