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Dead-zone logic in autonomic systems

Dead-zone logic in autonomic systems

Eze, Thaddeus and Anthony, Richard (2014) Dead-zone logic in autonomic systems. In: 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). Institute of Electrical and Electronics Engineers, Inc., Piscataway, NJ, USA, pp. 1-8. ISBN 9781479933471 (doi:10.1109/EAIS.2014.6867462)

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Abstract

Dead-Zone logic is a mechanism to prevent autonomic managers from unnecessary, inefficient and ineffective control brevity when the system is sufficiently close to its target state. It provides a natural and powerful framework for achieving dependable self-management in autonomic systems by enabling autonomic managers to smartly carry out a change (or adapt) only when it is safe and efficient to do so-within a particular (defined) safety margin. This paper explores and evaluates the performance impact of dead-zone logic in trustworthy autonomic computing. Using two case example scenarios, we present empirical analyses that demonstrate the effectiveness of dead-zone logic in achieving stability, dependability and trustworthiness in adaptive systems. Dynamic temperature target tracking and autonomic datacentre resource request and allocation management scenarios are used. Results show that dead-zone logic can significantly enhance the trustability of autonomic systems.

Item Type: Conference Proceedings
Title of Proceedings: 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)
Additional Information: [1] Paper presented and in proceedings of 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2014. Held Bildungszentrum St. MagdalenaSchatzweg 177 Linz, Austria. Dates: 2-4 June 2014.
Uncontrolled Keywords: autonomic system, autonomic techniques, datacentre, dead-zone logic, dependable system, stability, trustworthiness
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Related URLs:
Last Modified: 18 Apr 2017 14:25
Selected for GREAT 2016: GREAT a
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/12931

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