Using Statistical Assertions to Guide Self-Adaptive Systems
Self-adaptive systems need to monitor themselves, to check their internal behaviour and design assumptions about runtime inputs and conditions. This kind of monitoring for self-adaptive systems can include collecting statistics about such systems themselves which can be computationally intensive (fo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | International Journal of Reconfigurable Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/724585 |
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| Summary: | Self-adaptive systems need to monitor themselves, to check their internal behaviour and design assumptions about runtime inputs and conditions. This kind of monitoring for self-adaptive systems can include collecting statistics about such systems themselves which can be computationally intensive (for detailed statistics) and hence time consuming, with possible negative impact on self-adaptive response time. To mitigate this limitation, we extend the technique of in-circuit runtime assertions to cover statistical assertions in hardware. The presented designs implement several statistical operators that can be exploited by self-adaptive systems; a novel optimization is developed for reducing the number of pairwise operators from ON to OlogN. To illustrate the practicability and industrial relevance of our proposed approach, we evaluate our designs, chosen from a class of possible application scenarios, for their resource usage and the tradeoffs between hardware and software implementations. |
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| ISSN: | 1687-7195 1687-7209 |