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001-es BibID:BIBFORM062529
035-os BibID:Zbl.06476013
Első szerző:Gál Zoltán (informatikus)
Cím:Surprise event detection of the supercomputer execution queues / Zoltán Gál, Tibor Tajti, György Terdik
ISSN:1787-5021 1878-6117
Megjegyzések:Huge amount of data is generated by and collected from the IoT (Internet of Things) physical and virtual devices. These sets of data series reflect in complex form the state of a given system in multidimensional space. Healthiness evaluation of a given system implies state analysis with enhanced methods. Special events can appear during the execution of jobs in a supercomputer (HPC - High Performance Computer) system. Depending on the HPC architecture hundreds or thousands of computation nodes are working in parallel. The scheduler of the HPC front-end node manages different queues (parallel, serial, test, etc.) of the job execution. The multitude of data series captured periodically with several tens of thousands of samples creates a set of several dozen variables for each computation node. The healthiness of the whole HPC system is a temporal concept in the term of 2D or 4D multidimensional time-space domains. In this paper we propose a healthiness evaluation method for each execution queue of a two different HPC system with 20 TFLOP/s and 5 TFLOP/s computation capacities, respectively. Time independent community structure is determined and controlled based on multiple similarity measures and ANN (Artificial Neural Network) based SOM (Self-Organized Map) algorithm. For each cluster of variables is determined a representing variable, including time specific and global characteristics of the own cluster. The resulting set of representing variables contains less than ten dissimilar time series. Wavelet methods are used for extreme event detection in time of each representing variable. The surprise event detection in time of the HPC execution queues is based on the simultaneity of extreme event fingerprints.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény hazai lapban
High Performance Computer
Complex Event Processing
Event Stream Processing
Artificial Neural Networks
Megjelenés:Annales Mathematicae et Informaticae. - 44 (2015), p. 87-97. -
További szerzők:Tajti Tibor Gábor (1970-) (informatikus) Terdik György (1949-) (matematikus, informatikus)
Pályázati támogatás:TÁMOP-4.2.2.C-11/1/KONV-2012-0010
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001-es BibID:BIBFORM094453
035-os BibID:(WoS)000654007400011 (Scopu)85107458834
Első szerző:Korteby, Mohamed Amine (informatikus)
Cím:Multi dimensional analysis of sensor communication processes / Mohmamed Amine Korteby, Zoltán Gál, Péter Polgár
ISSN:1787-5021 1787-6117
Megjegyzések:The Internet of Things requires communication mechanism to be optimal not only from the data transfer but from the energy consumption point of view, too. One of the most analyzed types of the sensor network is Low Energy Adaptive Clustering Hierarchy (LEACH) system depending on the population density, algorithm of cluster head election, heterogeneity of the energy and physical position of the nodes, velocity of the sink node, data aggregation rate and size of the data frame. Complexity of the system has been analyzed based on status data series of 360 simulation cases. New family of wireless sensor network (WSN) system is proposed with name CB-LEACH, having better characteristics than the classical LEACH system. The service ability of sensor network and dependency properties was done with analytic technique based on Singular Value Decomposition (SVD). Using this method there were identified most important modes serving as basis to regenerate responses of the studied sensor systems. It was found that the number of significant modes is just six. The novelty of the paper is a proof of concept that SVD is a useful multidimensional tool which can be used for describing the behavior of the newly proposed CB-LEACH family of sensor network mechanisms.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény hazai lapban
Wireless sensor networks
Low Energy Adaptive Clustering Hierrarchy (LEACH)
classification analysis
Megjelenés:Annales Mathematicae et Informaticae. - 53 (2021), p. 169-182. -
További szerzők:Gál Zoltán (1966-) (informatikus) Polgár Péter (1996-) (informatikus)
Pályázati támogatás:EFOP-3.6.3-VEKOP-16-2017-00002
Internet cím:Szerző által megadott URL
Intézményi repozitóriumban (DEA) tárolt változat
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