Auflistung nach:
Auflistung Softwaretechnik-Trends 43(4) - 2023 nach Schlagwort "benchmark"
1 - 3 von 3
Treffer pro Seite
Sortieroptionen
- KonferenzbeitragBenchmarking Function Hook Latency in Cloud-Native Environments(Softwaretechnik-Trends Band 43, Heft 4, 2023) Kahlhofer, Mario; Kern, Patrick; Henning, Sören; Rass, StefanResearchers 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 dy namically patched or hooked applications. We present recommendations to mitigate these risks and demonstrate how an improper experimental setup can negatively impact latency measurements.
- KonferenzbeitragBenchmarking Stream Processing Frameworks for Large Scale Data Shuffling(Softwaretechnik-Trends Band 43, Heft 4, 2023) Henning, Sören; Vogel, Adriano; Leichtfried, Michael; Ertl, Otmar; Rabiser, RickDistributed stream processing frameworks help building scalable and reliable applications that perform transformations and aggregations on continuous data streams. We outline our ongoing research on designing a new benchmark for distributed stream processing frameworks. In contrast to other benchmarks, it focuses on use cases where stream processing frameworks are mainly used for redistributing data records to perform state-local aggregations, while the actual aggregation logic is considered as black-box software components. We describe our benchmark architecture based on a real-world use case, show how we imple mented it with four state-of-the-art frameworks, and give an overview of initial experimental results.
- KonferenzbeitragComparing the Performance of Data Processing Implementations(Softwaretechnik-Trends Band 43, Heft 4, 2023) Beierlieb, Lukas; Iffländer, Lukas; Prantl, Thomas; Kounev, SamuelThis paper compares the execution speed of R, Python, and Rust implementations in the context of data processing. A real-world data processing task in the form of an aggregation of benchmark measure ment results was implemented in each language, and the execution times were measured. Rust and Python showed significantly superior performance compared to the R implementation. Further, we compared the results of different Python interpreters (the most recent versions of CPython and PyPy), also resulting in measurable variations. Finally, a study of the effectiveness of multithreading was performed.