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https://doi.org/10.21256/zhaw-22690| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Resource management for cloud functions with memory tracing, profiling and autotuning |
| Autor/-in: | Spillner, Josef |
| et. al: | No |
| DOI: | 10.1145/3429880.3430094 10.21256/zhaw-22690 |
| Tagungsband: | Proceedings of the 2020 Sixth International Workshop on Serverless Computing |
| Seite(n): | 13 |
| Seiten bis: | 18 |
| Angaben zur Konferenz: | 21th International Middleware Conference, Delft, Netherlands (online), 7-11 December 2020 |
| Erscheinungsdatum: | 7-Dez-2020 |
| Verlag / Hrsg. Institution: | Association for Computing Machinery |
| ISBN: | 978-1-4503-8204-5 |
| Sprache: | Englisch |
| Schlagwörter: | Serverless computing; Vertical scaling; Model |
| Fachgebiet (DDC): | 004: Informatik |
| Zusammenfassung: | Application software provisioning evolved from monolithic designs towards differently designed abstractions including serverless applications. The promise of that abstraction is that developers are free from infrastructural concerns such as instance activation and autoscaling. Today's serverless architectures based on FaaS are however still exposing developers to explicit low-level decisions about the amount of memory to allocate for the respective cloud functions. In many cases, guesswork and ad-hoc decisions determine the values a developer will put into the configuration. We contribute tools to measure the memory consumption of a function in various Docker, OpenFaaS and GCF/GCR configurations over time and to create trace profiles that advanced FaaS engines can use to autotune memory dynamically. Moreover, we explain how pricing forecasts can be performed by connecting these traces with a FaaS characteristics knowledge base. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22690 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2020_Spillner_FaaS-memory-autotuning.pdf | Accepted Version | 421.31 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Spillner, J. (2020). Resource management for cloud functions with memory tracing, profiling and autotuning [Conference paper]. Proceedings of the 2020 Sixth International Workshop on Serverless Computing, 13–18. https://doi.org/10.1145/3429880.3430094
Spillner, J. (2020) ‘Resource management for cloud functions with memory tracing, profiling and autotuning’, in Proceedings of the 2020 Sixth International Workshop on Serverless Computing. Association for Computing Machinery, pp. 13–18. Available at: https://doi.org/10.1145/3429880.3430094.
J. Spillner, “Resource management for cloud functions with memory tracing, profiling and autotuning,” in Proceedings of the 2020 Sixth International Workshop on Serverless Computing, Dec. 2020, pp. 13–18. doi: 10.1145/3429880.3430094.
SPILLNER, Josef, 2020. Resource management for cloud functions with memory tracing, profiling and autotuning. In: Proceedings of the 2020 Sixth International Workshop on Serverless Computing. Conference paper. Association for Computing Machinery. 7 Dezember 2020. S. 13–18. ISBN 978-1-4503-8204-5
Spillner, Josef. 2020. “Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning.” Conference paper. In Proceedings of the 2020 Sixth International Workshop on Serverless Computing, 13–18. Association for Computing Machinery. https://doi.org/10.1145/3429880.3430094.
Spillner, Josef. “Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning.” Proceedings of the 2020 Sixth International Workshop on Serverless Computing, Association for Computing Machinery, 2020, pp. 13–18, https://doi.org/10.1145/3429880.3430094.
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