Affiliations: [a] Katholieke Universiteit Leuven, Belgium andLinnaeus University, Sweden
| [b]
Leiden University, The Netherlands and NORCE Norwegian Research Centre, Norway
| [c]
Johns Hopkins University, USA and NORCE Norwegian Research Centre, Norway
| [d]
University of Birmingham, UK and Southern University of Science and Technology, China
| [e]
NORCE Norwegian Research Centre, Norway
Correspondence:
[*]
Corresponding author: Danny Weyns, Katholieke Universiteit Leuven,
Belgium and Linnaeus University, Sweden. Tel: (+32)474-208251. E-mail:
[email protected].
Abstract: Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the continuous change they face, ranging from dynamic operating conditions, to changing goals, and technological progress. While we are able to engineer smart computing systems that autonomously deal with various types of changes, handling unanticipated changes requires system evolution, which remains in essence a human-centered process. This will eventually become unmanageable. To break through the status quo, we put forward an arguable opinion for the vision of self-evolving computing systems that are equipped with an evolutionary engine enabling them to evolve autonomously. Specifically, when a self-evolving computing systems detects conditions outside its operational domain, such as an anomaly or a new goal, it activates an evolutionary engine that runs online experiments to determine how the system needs to evolve to deal with the changes, thereby evolving its architecture. During this process the engine can integrate new computing elements that are provided by computing warehouses. These computing elements provide specifications and procedures enabling their automatic integration. We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions. To conclude, we highlight key research challenges to realize the vision of self-evolving computing systems.
