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Title: Data for: Elastic Application-Level Monitoring for Large Software Landscapes in the Cloud      
dateReleased:
07-03-2015
privacy:
information not avaiable
aggregation:
instance of dataset
dateCreated:
07-03-2015
refinement:
raw
ID:
doi:10.5281/ZENODO.19296
creators:
Fittkau, Florian
Hasselbring, Wilhelm
availability:
available
types:
other
description:
Application-level monitoring provides valuable, detailed insights into running applications. However, many approaches often only employ a single analysis application. This analysis application may become a performance bottleneck when monitoring several programs resulting in reduced monitoring quality or violated service level agreements of the monitored applications. We present an approach for elastic, distributed application-level monitoring for large software landscapes consisting of several hundreds of applications by utilizing cloud computing. Our approach dynamically inserts and removes worker levels to circumvent overloading the analysis master application without interrupting or pausing the actual live analysis of the monitored data. To evaluate our approach, we conduct an experiment in which we generate load - following a real workload pattern - on web applications in a 24 hour experiment. In our experiment, 160 monitored JPetStore instances generate roughly 20 million analyzed method calls per second in the peak. Furthermore, two worker levels are dynamically started and removed in line with the imposed workload on the monitored applications. The experiment shows that our monitoring approach is capable of live analyzing several millions of monitored method calls per second without overloading the analysis master application. This package provides our supplementary data.
accessURL: https://doi.org/10.5281/ZENODO.19296
storedIn:
Zenodo
qualifier:
not compressed
format:
HTML
accessType:
landing page
authentication:
none
authorization:
none
abbreviation:
ZENODO
homePage: https://zenodo.org/
ID:
SCR:004129
name:
ZENODO

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