Do google app engine's runtimes perform homogeneously? An empirical investigation for bonus computing

Zheng Li; Xue Guo

doi:10.1109/ACCESS.2018.2889400

Do google app engine's runtimes perform homogeneously? An empirical investigation for bonus computing

Zheng Li^*, Xue Guo

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

8 Downloads (Pure)

Abstract

Bonus computing is a new metacomputing form that takes advantage of free computing power from the public Cloud market. To maximize the value of free Cloud resources and facilitate dividing Bonus computing tasks, it would particularly be crucial to understand the performance of candidate Cloud services before using them in production. By offering free quotas in its standard environment, Google App Engine (GAE) has become a popular public Platform-As-A-Service (PaaS) for Bonus computing. Since GAE natively supports various programming languages with flexible configurations (e.g., region selection), it will be possible and valuable to squeeze GAE's free computing power if there is an optimal choice of its different runtimes. Following the performance evaluation methodology DoKnowMe, we implemented several versions of the Fibonacci(-like) calculation as benchmarks to fundamentally investigate GAE's standard environment. Our investigation results reveal that GAE does not support its runtime environments homogeneously in terms of their computation speed and memory efficiency. The heterogeneity could be related not only to the characteristics of different programming languages but also to the diverse GAE infrastructures. For example, Go runtime seems to be a well-Trade-off to satisfy Bonus computing among all the options, while the GAE service located in southamerica-east1 and us-central1 performs dramatically worse than that in the other regions.

Original language	English
Pages (from-to)	4698-4708
Number of pages	11
Journal	IEEE Access
Volume	7
DOIs	https://doi.org/10.1109/ACCESS.2018.2889400
Publication status	Published - 23 Dec 2018
Externally published	Yes

Bibliographical note

Funding Information:
This work was supported in part by CONICYT under Grant FONDECYT Iniciación 11180905, in part by the University of Concepción under Grant VRID INICIACION 218.093.017-1.0 IN, and in part by the National Natural Science Foundation of China (NSFC) under Grant 61572251.

Publisher Copyright:
© 2013 IEEE.

Keywords

Bonus computing
Google App Engine
performance evaluation
Platform as a Service
programming languages

ASJC Scopus subject areas

General Computer Science
General Materials Science
General Engineering

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10.1109/ACCESS.2018.2889400Licence: CC BY

Do google app engine's runtimes perform homogeneously? An empirical investigation for bonus computing
Copyright 2018 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 6.33 MBLicence: CC BY

Cite this

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title = "Do google app engine's runtimes perform homogeneously? An empirical investigation for bonus computing",

abstract = "Bonus computing is a new metacomputing form that takes advantage of free computing power from the public Cloud market. To maximize the value of free Cloud resources and facilitate dividing Bonus computing tasks, it would particularly be crucial to understand the performance of candidate Cloud services before using them in production. By offering free quotas in its standard environment, Google App Engine (GAE) has become a popular public Platform-As-A-Service (PaaS) for Bonus computing. Since GAE natively supports various programming languages with flexible configurations (e.g., region selection), it will be possible and valuable to squeeze GAE's free computing power if there is an optimal choice of its different runtimes. Following the performance evaluation methodology DoKnowMe, we implemented several versions of the Fibonacci(-like) calculation as benchmarks to fundamentally investigate GAE's standard environment. Our investigation results reveal that GAE does not support its runtime environments homogeneously in terms of their computation speed and memory efficiency. The heterogeneity could be related not only to the characteristics of different programming languages but also to the diverse GAE infrastructures. For example, Go runtime seems to be a well-Trade-off to satisfy Bonus computing among all the options, while the GAE service located in southamerica-east1 and us-central1 performs dramatically worse than that in the other regions.",

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Do google app engine's runtimes perform homogeneously? An empirical investigation for bonus computing

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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