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Migrating monolithic applications to function as a service

Authors: Hendrik M. Würz; Michel Krämer; Marvin Kaster; Arjan Kuijper;

Migrating monolithic applications to function as a service

Abstract

SummaryFunction as a service (FaaS) promises low operating costs, reduced complexity, and good application performance. However, it is still an open question how to migrate monolithic applications to FaaS. In this paper, we present a guideline for software designers to split monolithic applications into smaller functions that can be executed in a FaaS environment. This enables independent scaling of individual parts of the application. Our approach consists of three steps: We first identify the main tasks (and their subtasks) of the application to split. Then, we define the program flow to be able to tell which application tasks can be converted to functions and how they interact with each other. In the final step, we specify actual functions and possibly merge those that are too small and which would produce too much communication overhead or maintenance effort. Compared to existing work, our approach applies to applications of any size and results in functions that are small enough—but not too small—for efficient execution in a FaaS environment. We evaluate the usefulness of our approach by applying it to a real‐world application for the storage of geospatial data. We describe the experiences made and finish the paper with a discussion, conclusions, and ideas for future work.

Countries
Germany, Germany
Keywords

Research Line: Modeling (MOD), LTA: Generation, capture, processing, and output of images and 3D models, Branche: Bioeconomics and Infrastructure, Research Line: Computer graphics (CG), Architectural design, LTA: Scalable architectures for massive data sets, Design patterns, Branche: Information Technology, Cloud computing, Migration

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Top 10%
Top 10%
hybrid