Dataset Open Access

Data for: Scalable and Live Trace Processing with Kieker Utilizing Cloud Computing

Fittkau, Florian; Waller, Jan; Brauer, Peer; Hasselbring, Wilhelm

Knowledge of the internal behavior of applications often gets lost over the years. This circumstance can arise, for example, from missing documentation. Application-level monitoring, e.g., provided by Kieker, can help with the comprehension of such internal behavior. However, it can have large impact on the performance of the monitored system. High-throughput processing of traces is required by projects where millions of events per second must be processed live. In the cloud, such processing requires scaling by the number of instances.

In this paper, we present our performance tunings conducted on the basis of the Kieker monitoring framework to support high-throughput and live analysis of application-level traces. Furthermore, we illustrate how our tuned version of Kieker can be used to provide scalable trace processing in the cloud.

This is the dataset containing the results of our conducted benchmarks.

Files (6.6 GB)
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KiekerPalladioDays2013-HighThroughputTuningResults.zip.001
md5:d68c190e31060cb78c56557d3d5bd788
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KiekerPalladioDays2013-HighThroughputTuningResults.zip.002
md5:cd655d353778ae23aeb056ea1b62f3d8
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KiekerPalladioDays2013-HighThroughputTuningResults.zip.003
md5:54e70a8045d21ff837372431f3279c1d
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KiekerPalladioDays2013-HighThroughputTuningResults.zip.004
md5:2be6e34f6bce46b7a0af0f0e347120d3
582.1 MB Download
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