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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
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: ACM
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.
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in collections:Publikationen School of Engineering

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