Abstract
Goal of the paper is to design dynamic, model-driven solution for agile service and network monitoring based on metrics suitable for managing large infrastructures and clustered applications in baremetal or cloud environments. The solution uses decetralised model where each node computes its states and passes the results to upper level cluster monitoring services. This is opposite approach than traditional monitoring where all logic is stored on central monitoring node or cluster of nodes.
Our proposed Monitoring solution offers automation of repetitive administration tasks and makes creation of monitoring infrastructure part of routine configuration tasks and make use of meta-data models already in place. Various monitoring scenarious are covered as Cluster and inter datacenter monitoring, advanced time-series analytics, corellations and trending with prediction algorythms and querying log event databases for unusual patterns. Emphasis is given to limiting the amount of output when failures occur presented to human operators, reducing the time to solve the problem and automating solution of most common issues.
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Acknowledgement
The support of Czech Science Foundation GACR 15-11724S DEPIES is gratefully acknowledged. This work and the contribution were also supported by project of specic science, Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic.
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Komarek, A., Pavlik, J., Mercl, L., Sobeslav, V. (2018). Metric Based Cloud Infrastructure Monitoring. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_37
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DOI: https://doi.org/10.1007/978-3-319-69835-9_37
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