Home > Research > Publications & Outputs > Distributed Emergent Software

Electronic data

  • des

    Accepted author manuscript, 2.45 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Distributed Emergent Software: Assembling, Perceiving and Learning Systems at Scale

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date1/08/2019
Host publication2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
PublisherIEEE
Pages127-136
Number of pages10
ISBN (electronic)9781728127316
ISBN (print)9781728127323
<mark>Original language</mark>English

Abstract

Emergent software systems take a reward signal, an environment signal, and a collection of possible behavioural compositions implementing the system logic in a variety of ways, to learn in real-time how best to assemble a system. This reduces the burden of complexity in systems building by making human programmers responsible only for developing potential building blocks while the system determines how best to use them in its deployment conditions – with no architectural models or training regimes. In this paper we generalise the approach to distributed systems, to demonstrate for the first time how a single reward signal can form the basis of complex decision making about how to compose the software running on each host machine, where to place each sub-unit of software, and how many instances of each sub-unit should be created. We provide an overview of the necessary system mechanics to support this concept, and discuss the key challenges in machine learning needed to realise it. We present our current implementation in both datacentre and pervasive computing environments, with experimental results for a baseline learning approach.