Serverless computing is a new cloud computing
execution model. It offers little operational and administrative overhead, function activation on-demand, and significantly
reduced resource consumption. For that reason, it is studied
heavily in recent literature by means of handling incoming
traffic, applicability for different tasks, and overall performance.
Especially the introduction of states, where functionality is kept in
CPU or memory can improve performance significantly. However,
an in-depth analysis of the resource demand by among others,
CPU and memory usage, latency, and energy consumption is
still a blank spot in literature. To this end, we propose a
serverless lifecycle model with intermediate states and deploy
an image processing test setup in the edge cloud to test it.
Furthermore, we measure important resource metrics of all states
and state transitions of the model. This helps us to answer the
question, whether keeping functions available in specific states
of a serverless computing instance can improve performance
without massive negative influence on the resource requirements.
%0 Conference Paper
%1 info3-article-2023-4
%A Nguyen, TrungKien
%A Loh, Frank
%A Nguyen, Tung
%A Doan, Duong
%A Huu Thanh, Nguyen
%A Hoßfeld, Tobias
%B IEEE International Conference on Communications 2023 - GreenNet Workshop
%D 2023
%K myown
%T Serverless Computing Lifecycle Model for Edge Cloud Deployments
%X Serverless computing is a new cloud computing
execution model. It offers little operational and administrative overhead, function activation on-demand, and significantly
reduced resource consumption. For that reason, it is studied
heavily in recent literature by means of handling incoming
traffic, applicability for different tasks, and overall performance.
Especially the introduction of states, where functionality is kept in
CPU or memory can improve performance significantly. However,
an in-depth analysis of the resource demand by among others,
CPU and memory usage, latency, and energy consumption is
still a blank spot in literature. To this end, we propose a
serverless lifecycle model with intermediate states and deploy
an image processing test setup in the edge cloud to test it.
Furthermore, we measure important resource metrics of all states
and state transitions of the model. This helps us to answer the
question, whether keeping functions available in specific states
of a serverless computing instance can improve performance
without massive negative influence on the resource requirements.
@inproceedings{info3-article-2023-4,
abstract = {Serverless computing is a new cloud computing
execution model. It offers little operational and administrative overhead, function activation on-demand, and significantly
reduced resource consumption. For that reason, it is studied
heavily in recent literature by means of handling incoming
traffic, applicability for different tasks, and overall performance.
Especially the introduction of states, where functionality is kept in
CPU or memory can improve performance significantly. However,
an in-depth analysis of the resource demand by among others,
CPU and memory usage, latency, and energy consumption is
still a blank spot in literature. To this end, we propose a
serverless lifecycle model with intermediate states and deploy
an image processing test setup in the edge cloud to test it.
Furthermore, we measure important resource metrics of all states
and state transitions of the model. This helps us to answer the
question, whether keeping functions available in specific states
of a serverless computing instance can improve performance
without massive negative influence on the resource requirements.},
added-at = {2023-06-03T21:56:30.000+0200},
author = {Nguyen, TrungKien and Loh, Frank and Nguyen, Tung and Doan, Duong and Huu Thanh, Nguyen and Hoßfeld, Tobias},
biburl = {https://www.bibsonomy.org/bibtex/251fc8c0422c687edbe4d3801b57d49f6/uniwue_info3},
booktitle = {IEEE International Conference on Communications 2023 - GreenNet Workshop},
interhash = {f5522951c868a7fd48d8db25218c6103},
intrahash = {51fc8c0422c687edbe4d3801b57d49f6},
keywords = {myown},
timestamp = {2023-06-03T21:57:34.000+0200},
title = {Serverless Computing Lifecycle Model for Edge Cloud Deployments},
year = 2023
}