Reference : Loginson: a transform and load system for very large-scale log analysis in large IT i...
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/37225
Loginson: a transform and load system for very large-scale log analysis in large IT infrastructures
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Vega Moreno, Carlos Gonzalo [Universidad Autónoma de Madrid > Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones]
Roquero, Paula [Universidad Autónoma de Madrid > Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones]
Leira, Rafael [Universidad Autónoma de Madrid > Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones]
Gonzalez, Iván [Universidad Autónoma de Madrid > Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones]
Aracil, Javier [Universidad Autónoma de Madrid > Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones]
2017
Journal of Supercomputing
Springer New York LLC
73
9
3879-3900
Yes (verified by ORBilu)
International
09208542
[en] Data repositories for log collection; Large datacenters; Log analysis; Operational intelligence
[en] Nowadays, most systems and applications produce log records that are useful for security and monitoring purposes such as debugging programming errors, checking system status, and detecting configuration problems or even attacks. To this end, a log repository becomes necessary whereby logs can be accessed and visualized in a timely manner. This paper presents Loginson, a high-performance log centralization system for large-scale log collection and processing in large IT infrastructures. Besides log collection, Loginson provides high-level analytics through a visual interface for the purpose of troubleshooting critical incidents. We note that Loginson outperforms all of the other log centralization solutions by taking full advantage of the vertical scalability, and therefore decreasing Capital Expenditure (CAPEX) and Operating Expense (OPEX) costs for deployment scenarios with a huge volume of log data. © 2017, Springer Science+Business Media New York.
http://hdl.handle.net/10993/37225
10.1007/s11227-017-1990-1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014539558&doi=10.1007%2fs11227-017-1990-1&partnerID=40&md5=2a520718fe60ea4ec7f943c358baea8f

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