Andreas Kumlehn

We don’t have enough information about this author to calculate their statistics. If you think this is an error let us know.
Learn More
Distributed applications, cloud systems, the Internet of Things, etc. are generating increasing amounts of operational data, such as CPU loads, thread states, memory consumptions, method runtimes, or logs. Many tools continuously collect and analyze such data that is best represented as time series. Typical analyses try to find and localize runtime(More)
Auch das beste Software-System kann Anomalien im Laufzeitverhalten aufweisen, die nach einiger Zeit in Fehlerzustände münden können. Bekannte Werkzeuge überwachen kontinuierlich, ob Laufzeiten wie z.B. CPU-Last oder Antwortzeiten manuell gesetzte Schwellwerte überschreiten. Das hat zwei Nachteile: (a) Starr vorgegebene Schwellwerte und damit starre(More)
Anomalies in the runtime behavior of software systems, especially in distributed systems, are inevitable, expensive, and hard to locate. To detect and correct such anomalies (like instability due to a growing memory consumption, failure due to load spikes, etc.) one has to automatically collect, store, and analyze the operational data of the runtime(More)
  • 1