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

Mechanical strain stimulates COPII‐dependent secretory trafficking via Rac1

Cells are constantly exposed to various chemical and physical stimuli. While much has been learned about the biochemical factors that regulate secretory trafficking from the endoplasmic reticulum (ER), much less is known about whether and how this trafficking is subject to regulation by mechanical signals. Here, we show that subjecting cells to mechanical strain both induces the formation of ER exit sites (ERES) and accelerates ER‐to‐Golgi trafficking. We found that cells with impaired ERES function were less capable of expanding their surface area when placed under mechanical stress and were more prone to develop plasma membrane defects when subjected to stretching. Thus, coupling of ERES function to mechanotransduction appears to confer resistance of cells to mechanical stress. Furthermore, we show that the coupling of mechanotransduction to ERES formation was mediated via a previously unappreciated ER‐localized pool of the small GTPase Rac1. Mechanistically, we show that Rac1 interacts with the small GTPase Sar1 to drive budding of COPII carriers and stimulates ER‐to‐Golgi transport. This interaction therefore represents an unprecedented link between mechanical strain and export from the ER

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

The α‐arrestin family of ubiquitin ligase adaptors links metabolism with selective endocytosis

International audienc

Complementary α-arrestin - Rsp5 ubiquitin ligase complexes control selective nutrient transporter endocytosis in response to amino acid availability

How cells adjust transport across their membranes is incompletely understood. Previously, we have shown that S.cerevisiae broadly re-configures the nutrient transporters at the plasma membrane in response to amino acid availability, through selective endocytosis of sugar- and amino acid transporters (AATs) (Müller et al., 2015). A genome-wide screen now revealed that Art2/Ecm21, a member of the α-arrestin family of Rsp5 ubiquitin ligase adaptors, is required for the simultaneous endocytosis of four AATs and induced during starvation by the general amino acid control pathway. Art2 uses a basic patch to recognize C-terminal acidic sorting motifs in these AATs and instructs Rsp5 to ubiquitinate proximal lysine residues. In response to amino acid excess, Rsp5 instead uses TORC1-activated Art1 to detect N-terminal acidic sorting motifs within the same AATs, which initiates exclusive substrate-induced endocytosis of individual AATs. Thus, amino acid availability activates complementary α-arrestin-Rsp5-complexes to control selective endocytosis for nutrient acquisition