Resource management for cloud functions with memory tracing, profiling and autotuning

Spillner, Josef

doi:10.1145/3429880.3430094

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22690

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Resource management for cloud functions with memory tracing, profiling and autotuning
Authors:	Spillner, Josef
et. al:	No
DOI:	10.1145/3429880.3430094 10.21256/zhaw-22690
Proceedings:	Proceedings of the 2020 Sixth International Workshop on Serverless Computing
Page(s):	13
Pages to:	18
Conference details:	21th International Middleware Conference, Delft, Netherlands (online), 7-11 December 2020
Issue Date:	7-Dec-2020
Publisher / Ed. Institution:	Association for Computing Machinery
ISBN:	978-1-4503-8204-5
Language:	English
Subjects:	Serverless computing; Vertical scaling; Model
Subject (DDC):	004: Computer science
Abstract:	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
Fulltext version:	Accepted version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Appears in collections:	Publikationen School of Engineering

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2020_Spillner_FaaS-memory-autotuning.pdf	Accepted Version	421.31 kB	Adobe PDF	View/Open

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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.