Perspectives on Anomaly and Event Detection in Exascale Systems

被引：2
|
作者：
Iuhasz, Gabriel [1 ,2 ]
Petcu, Dana [1 ,2 ]
机构：
[1] Intitute E Austria Timisoara, Timisoara, Romania
[2] West Univ Timisoara, Timisoara, Romania
来源：
2019 IEEE 5TH INTL CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY) / IEEE INTL CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING (HPSC) / IEEE INTL CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS) | 2019年
关键词：
exascale; machine learning; anomaly; distributed; monitoring; PERFORMANCE;
D O I：
10.1109/BigDataSecurity-HPSC-IDS.2019.00051
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The design and implementation of exascale system is nowadays an important challenge. Such a system is expected to combine HPC with Big Data methods and technologies to allow the execution of scientific workloads which are not tractable at this present time. In this paper we focus on an event and anomaly detection framework which is crucial in giving a global overview of a exascale system (which in turn is necessary for the successful implementation and exploitation of the system). We propose an architecture for such a framework and show how it can be used to handle failures during job execution.
引用
收藏
页码：225 / 229
页数：5
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