Hardware Performance Counters Data for Prediction of Potential Failure in Embedded Systems
Hardware Performance Counters Data for Prediction of Potential Failure in Embedded Systems
Raw data of hardware performance counters from benchmarks used in developing the early detection and prediction algorithm as well as proof-of-concept of a hardware detector for PhD Thesis entitled "Predicting Potential Failure in Real-Time through Monitoring and Detection of Anomalous Behaviour using Hardware Performance Counters".
Hardware Performance Counters, MiBench
University of Southampton
Woo, Lai Leng
ee042648-77bc-4b5d-979e-a44b302a7ad9
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Woo, Lai Leng
ee042648-77bc-4b5d-979e-a44b302a7ad9
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Woo, Lai Leng
(2019)
Hardware Performance Counters Data for Prediction of Potential Failure in Embedded Systems.
University of Southampton
doi:10.5258/SOTON/D1147
[Dataset]
Abstract
Raw data of hardware performance counters from benchmarks used in developing the early detection and prediction algorithm as well as proof-of-concept of a hardware detector for PhD Thesis entitled "Predicting Potential Failure in Real-Time through Monitoring and Detection of Anomalous Behaviour using Hardware Performance Counters".
Text
README_D1147.txt
- Text
Archive
Bitcount_5000ns.zip
- Dataset
Archive
Bitcount_100000ns.zip
- Dataset
Archive
Dijkstra_5000ns.zip
- Dataset
Archive
Dijkstra_100000ns.zip
- Dataset
Archive
FFT_5000ns.zip
- Dataset
Archive
FFT_100000ns.zip
- Dataset
Archive
Matlab_Code.zip
- Dataset
Archive
QSort_100000ns.zip
- Dataset
Archive
Stringsearch_100000ns.zip
- Dataset
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More information
Published date: 2019
Keywords:
Hardware Performance Counters, MiBench
Identifiers
Local EPrints ID: 435760
URI: http://eprints.soton.ac.uk/id/eprint/435760
PURE UUID: cfc6f7ba-95d0-44e8-8d5b-6faf68f15c72
Catalogue record
Date deposited: 19 Nov 2019 17:32
Last modified: 06 May 2023 01:46
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Contributors
Creator:
Lai Leng Woo
Research team head:
Mark Zwolinski
Research team head:
Basel Halak
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