Experiments with a machine-centric approach to realise distributed emergent software systems

Rodrigues Filho, Roberto and Porter, Barry Francis (2016) Experiments with a machine-centric approach to realise distributed emergent software systems. In: ARM 2016 Proceedings of the 15th International Workshop on Adaptive and Reflective Middleware. ACM, New York. ISBN 9781450346627

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Abstract

Modern distributed systems are exposed to constant changes in their operating environment, leading to high uncertainty. Self-adaptive and self-organising approaches have become a popular solution for runtime reactivity to this uncertainty. However, these approaches use predefined, expertly-crafted policies or models, constructed at design-time, to guide system (re)configuration. They are human-centric, making modelling or policy-writing difficult to scale to increasingly complex systems; and are inflexible in their ability to deal with the unexpected at runtime (e.g. conditions not captured in a policy). We argue for a machine-centric approach to this problem, in which the desired behaviour is autonomously learned and emerges at runtime from a large pool of small alternative components, as a continuous reaction to the observed behaviour of the software and the characteristics of its operating environment. We demonstrate our principles in the context of data-centre software, showing that our approach is able to autonomously coordinate a distributed infrastructure composed of emergent web servers and a load balancer. Our initial results validate our approach, showing autonomous convergence on an optimal configuration, and also highlight the open challenges in providing fully machine-led distributed emergent software systems.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© ACM, 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Adaptive and Reflective Middleware, 2016 http://dx.doi.org/10.1145/3008167.3008168
ID Code:
83367
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Deposited On:
20 Dec 2016 11:42
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Sep 2020 06:15