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

Type Dataset Fittkau, Florian, Waller, Jan, Brauer, Peer, Hasselbring, Wilhelm (2013): Data for: Scalable and Live Trace Processing with Kieker Utilizing Cloud Computing. Zenodo. Dataset. https://zenodo.org/record/7622

Authors: Fittkau, Florian (Kiel University, Kiel, Germany) ; Waller, Jan (Kiel University, Kiel, Germany) ; Brauer, Peer (Kiel University, Kiel, Germany) ; Hasselbring, Wilhelm (Kiel University, Kiel, Germany) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

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.

More information

• DOI: 10.5281/zenodo.7622

Subjects

• Kieker, ExplorViz, MooBench, Benchmarking, Trace Processing, Cloud Computing

Dates

• Publication date: 2013
• Issued: November 28, 2013

Rights

• https://creativecommons.org/publicdomain/zero/1.0/legalcode Creative Commons Zero v1.0 Universal
• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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