Benchmarking Function Hook Latency in Cloud-Native Environments

Researchers and engineers are increasingly adopting cloud-native technologies for application development and performance evaluation. While this has improved the reproducibility of benchmarks in the cloud, the complexity of cloud-native environments makes it difficult to run benchmarks reliably. Cloud-native applications are often instrumented or altered at runtime, by dynamically patching or hooking them, which introduces a significant performance overhead. Our work discusses the benchmarking-related pitfalls of the dominant cloud-native technology, Kubernetes, and how they affect performance measurements of dynamically patched or hooked applications. We present recommendations to mitigate these risks and demonstrate how an improper experimental setup can negatively impact latency measurements.Comment: to be published in the 14th Symposium on Software Performance (SSP 2023), source code available at https://github.com/dynatrace-research/function-hook-latency-benchmarkin

Applying Optimizations for Dynamically-typed Languages to Java

While Java is a statically-typed language, some of its features make it behave like a dynamically-typed language at run time. This includes Java’s boxing of primitive values as well as generics, which rely on type erasure. This paper investigates how runtime technology for dynamically-typed languages such as JavaScript and Python can be used for Java bytecode. Using optimistic optimizations, we specialize bytecode instructions that access references in such a way, that they can handle primitive data directly and also specialize data structures in order to avoid boxing for primitive types. Our evaluation shows that these optimizations can be successfully applied to a statically-typed language such as Java and can also improve performance significantly. With this approach, we get an efficient implementation of Java's generics, avoid changes to the Java language, and maintain backwards compatibility, allowing existing code to benefit from our optimization transparently

Exploring Supervised Event Prediction in Multi-System Monitoring

Diese Arbeit untersucht Monitoringdaten aus realen Software-Infrastrukturen, diskutiert Vorverarbeitungsschritte und erstellt Vorhersagemodelle mit neuronalen Netzen. Die Identifizierung von Performanceproblemen in großen Softwaresystemen ist entscheidend, um Systemausfälle zu vermeiden. Administratoren und Benutzer würden sehr profitieren, wenn man Vorfälle im Voraus vorhersagen könnte. Reale Monitoringdaten sind jedoch oft unstrukturiert und unrein. Wir analysieren Monitoringdaten von 250 Softwaresystemen, fassen notwendige Vorverarbeitungsschritte zusammen und evaluieren, ob Infrastrukturmetriken wie CPU-Last oder RAM-Nutzung verwendet werden können, um performance-kritische Ereignisse in Softwaresystemen vorherzusagen. Unsere Ergebnisse zeigen, dass unskalierte Rohdaten eine Vorhersagegenauigkeit von bis zu 72 % und ein F1 Maß von bis zu 75 % erreichen. Der Einsatz eines solchen Vorhersagesystems in einer realen Umgebung bleibt jedoch aufgrund des großen Klassenungleichgewichts immer noch eine Herausforderung.This thesis examines monitoring data from real-world software infrastructures, discusses pre-processing steps, and creates predictive models with neural networks. Identifying performance problems in large-scale software systems is crucial to prevent system outages. Administrators and users would benefit greatly if we could predict incidents in advance. However, real-world monitoring data is often unstructured and impure. We analyze monitoring data from 250 software systems, summarize necessary pre-processing steps, and evaluate whether infrastructure metrics, such as CPU load or RAM usage, can be used to predict performance-critical events in software systems. Our results show that unscaled, raw data achieves prediction accuracies of up to 72 % and F1 scores of up to 75 %. However, implementing such a prediction system in a real-world environment remains challenging due to the large class imbalance.eingereicht von Mario Kahlhofer, BScUniversität Linz, Masterarbeit, 2019(VLID)440344