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Evolving robust networks for systems-of-systems: is it viable for large networks?

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Abstract

Software systems that rely on ad-hoc networks are increasingly complex and prevalent. Some of these systems provide vital functionality to emergency services, military operations, and disaster relief; such systems may have significant impact on the safety of people involved in those operations. It is therefore important that those networks support critical software requirements, including those for latency of packet transfer. If a network ceases to meet the software’s requirements (e.g. due to a link failure) then engineers must be able to understand it well enough to reconfigure the network and restore it to a requirement-satisfying state. Given a complex network, it is difficult for a human to do this, especially under time pressure. Tool support would therefore be valuable. This paper presents a search-based tool which takes as input a network defined using the Network Description Language (NDL), annotated with a set of network-hosted applications and a set of latency requirements between each. The tool can evolve variants of the network configuration which meet the requirements and are robust to single link failures, using multi-objective optimisation to resolve the conflicting demands of robustness and low resource use. We have investigated how the Genetic Algorithm (GA) can be configured to find good results, and how the time taken to evolve networks scales with the size of the network. We conclude that the approach is effective for small networks (tens of nodes) but scales poorly beyond that.

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Notes

  1. http://cs.gmu.edu/~7Eeclab/projects/ecj/

  2. http://www-users.cs.york.ac.uk/rda/EvolvingRobustSoSESEVersion.zip

  3. http://www-users.cs.york.ac.uk/rda/NDLWriterESEPaperVersion.zip

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Acknowledgments

The authors wish to thank Jeroen van der Ham of the University of Amsterdam for assistance with NDL, and David White of the University of Glasgow for discussions about issues with scaling and potential bottlenecks within the memory management of the Java Virtual Machine.

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Authors and Affiliations

  1. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

    Jonathan M. Aitken

  2. Department of Computer Science, University of York, York, UK

    Rob Alexander, Tim Kelly & Simon Poulding

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  1. Jonathan M. Aitken
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  2. Rob Alexander
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  3. Tim Kelly
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  4. Simon Poulding
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Corresponding authors

Correspondence to Jonathan M. Aitken or Rob Alexander.

Additional information

Communicated by: Gordon Fraser and Jerffeson Teixeira de Souza

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Aitken, J.M., Alexander, R., Kelly, T. et al. Evolving robust networks for systems-of-systems: is it viable for large networks?. Empir Software Eng 19, 1502–1530 (2014). https://doi.org/10.1007/s10664-013-9295-3

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