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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22690
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dc.contributor.authorSpillner, Josef-
dc.date.accessioned2021-06-23T08:34:13Z-
dc.date.available2021-06-23T08:34:13Z-
dc.date.issued2020-12-07-
dc.identifier.isbn978-1-4503-8204-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22690-
dc.description.abstractApplication 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.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computing Machineryde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectServerless computingde_CH
dc.subjectVertical scalingde_CH
dc.subjectModelde_CH
dc.subject.ddc004: Informatikde_CH
dc.titleResource management for cloud functions with memory tracing, profiling and autotuningde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1145/3429880.3430094de_CH
dc.identifier.doi10.21256/zhaw-22690-
zhaw.conference.details21th International Middleware Conference, Delft, Netherlands (online), 7-11 December 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end18de_CH
zhaw.pages.start13de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 2020 Sixth International Workshop on Serverless Computingde_CH
zhaw.webfeedService Engineeringde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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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