# Towards an Energy-Aware Multi-objective Scheduling Policy for Server-based Edge-Cloud Continuum

Anderson Andrei Da Silva, Yiannis Georgiou, Michael Mercier, Grégory Mounié, Denis Trystram

## ► To cite this version:

Anderson Andrei Da Silva, Yiannis Georgiou, Michael Mercier, Grégory Mounié, Denis Trystram. "Towards an Energy-Aware Multi-objective Scheduling Policy for Server-based Edge-Cloud Continuum." COMPAS 2023 - Conférence francophone d'informatique en Parallélisme, Architecture et Système, Jul 2023, Annecy, France. pp.1-21, 2023. [hal-04244625](https://hal HAL Id: hal-04244625)

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# Towards an Energy-Aware Multi-objective Scheduling Policy for Server-based Edge-Cloud Continuum

Compas 2023

Anderson Andrei Da Silva¹, ², Y. Georgiou², M. Mercier², G. Mounié¹, D. Trystram¹.

¹Université Grenoble Alpes, ²Ryax Technologies

Contact: anderson-andrei.da-silva@inria.fr, silva.andersonandrei@gmail.com

https://andersonandrei.github.io/

4-7 of July of 2023

UGA
Université
Grenoble Alpes

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ryax
technologies

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MIA
Grenoble Alpes

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PHYSICS
## Edge-Cloud Continuum
### Infrastructure Level

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Cloud clusters

Edge clusters
or Fog

Edge
resources

Legend

Edge-Cloud Infrastructure Connections

Edge Resource Moving Connections
User's environment view

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Developer

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Functions

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Code

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Few

Settings

D. Scientists

Physicists

...

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Platform Provider's View

Scheduler

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Functions Execution Time

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Edge-Cloud Continuum
Infrastructure Level

Cloud clusters

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Scheduling Objective

Edge clusters
or Fog

Edge resources
<figure><img src="image_19.png" /></figure>

Towards a Multi-objective Scheduling Policy for
Serverless-based Edge-Cloud Continuum

The 23rd IEEE/ACM International Symposium on Cluster,
Cloud and Internet Computing
Bangalore, India — May 1-4, 2023

L. Angelelli¹, Anderson Andrei Da Silva¹, ², Y. Georgiou²,

M. Mercier², G. Mounié¹, D. Trystram¹.

¹Université Grenoble Alpes, ²Ryax Technologies

Reproducible
Artifacts

https://andersonandrei.github.io/

https://gitlab.com/andersonandrei/foa-a-multi-objective-scheduling-policy-for-serverless

Thanks to the Intelligence of the Multi-disciplinary Institute on Artificial Intelligence MIAI at Grenoble Alpes (ANR-19-P3IA-0003),
to the European Union's Project H2020 PHYSICS (GA 101017047), and to the Grid'5000 testbed (https://www.grid5000.fr).
Motivation 1 and a multi-objective approach

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# FOA: Function Orchestration Algorithm

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# FOA: Function Orchestration Algorithm

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## Assumptions:

1) Functions and environments are independent and known in advance;

2) Their cost and processing time on each machine are known;

3) The dependency between functions and environments is known.

* *The Minimum Cost Integral Matching* phase uses the Dual-Approx. Algorithm of Shmoys and Tardos [D. B. Shmoys and E. Tardos, "An approximation algorithm for the generalized assignment problem," *Math. Program.*, vol. 62, no. 1-3, p.461-474, Feb. 1993].
• Adaptation of the benchmark FunctionBench <fnref n="1" />,

• Deployment of OpenWhisk <fnref n="2" /> on GRID5000 <fnref n="3" />,

• Measurement and calibration of results.

• Evaluation of scheduling policies FOA, K8S ImageLocality (baseline):

• In a simulated environment,

• On top of Batsim/ Simgrid [H.Casanova et.al, 2014].

<footnote n="1">¹ https://github.com/kmu-bigdata/serverless-faas-workbench ,</footnote>

<footnote n="2">² https://openwhisk.apache.org/,</footnote>

<footnote n="3">³ https://www.grid5000.fr .</footnote>
<table><caption>Table 1 Design of Experiments. Total of 1620 experiments.</caption><thead><tr><td>Parameters</td><td>Values</td></tr></thead><tbody><tr><td>Workload Size</td><td>200, 600, 1000</td></tr><tr><td>Platform Size</td><td>100, 300, 500</td></tr><tr><td>Heterogeneity Level</td><td>3, 5, 7</td></tr><tr><td>Scheduling Policies</td><td>FOA, <i>K8S ImageLocality</i></td></tr><tr><td>Random Seeds</td><td>30</td></tr></tbody></table>
<table><caption>Table 1 Design of Experiments. Total of 1620 experiments.</caption><thead><tr><td>Parameters</td><td>Values</td></tr></thead><tbody><tr><td>Workload Size</td><td>200, 600, 1000</td></tr><tr><td>Platform Size</td><td>100, 300, 500</td></tr><tr><td>Heterogeneity Level</td><td>3, 5, 7</td></tr><tr><td>Scheduling Policies</td><td>FOA, K8S ImageLocality</td></tr><tr><td>Random Seeds</td><td>30</td></tr></tbody></table>

<table><caption>Table 2: Functions adapted from FunctionBench.</caption><thead><tr><td>Function</td><td>Input Values</td><td>Unit</td></tr></thead><tbody><tr><td>Chameleon</td><td>2.10<sup>3</sup>, 3.10<sup>3</sup></td><td>Matrix size</td></tr><tr><td>Float operation</td><td>10.10<sup>7</sup>, 20.10<sup>7</sup>, 30.10<sup>7</sup></td><td>Operations</td></tr><tr><td>Image processing</td><td>60, 40</td><td>Image size in MB</td></tr><tr><td>Linpack</td><td>5.10<sup>3</sup>, 6.10<sup>3</sup></td><td>Matrix size</td></tr><tr><td>Matrix Multiplication</td><td>3.10<sup>3</sup>, 4.10<sup>3</sup></td><td>Matrix size</td></tr><tr><td>Model Training</td><td>50, 100</td><td>Dataset size</td></tr><tr><td>Pyaes</td><td>3.10<sup>3</sup>, 4.10<sup>3</sup>, 5.10<sup>3</sup></td><td>Length of message</td></tr><tr><td>Video Processing</td><td>30, 50, 100</td><td>Video size in MB</td></tr></tbody></table>
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* Cloud greedy algorithms may not profit from heterogeneity,

* FOA outperforms our baseline for both data transfers and makespan criteria, in addition to the system utilization by up to two orders of magnitudes,

* FOA minimizes cold start delays and speed up functions' execution time,

* However, FOA is very time-consuming.
Motivation 2 = Motivation 1 + Energy Consumption

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## Assumptions:

1) Functions and environments are independent and known in advance;

2) Their cost, energy consumption and processing time on each machine are known;

3) The dependency between functions and environments is known.
Energy Consumption on Serverless Platforms

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Energy Consumption on Serverless Platforms

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Function — Inpack_5000 — Inpack_6000

Function — chameleon_2000 — chameleon_4000
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* Cloud greedy algorithms may not profit from heterogeneity,

* FOA-e outperforms our baseline for both data transfers and energy consumption criteria, in addition to the system utilization,

* FOA-e minimizes cold start delays and speed up functions' execution time,

* FOA-e has a scheduling time in order of seconds.
* Cloud greedy algorithms may not profit from heterogeneity,

* FOA-e outperforms our baseline for both data transfers and energy consumption criteria, in addition to the system utilization,

* FOA-e minimizes cold start delays and speed up functions' execution time,

* FOA-e has a scheduling time in order of seconds.

Work in progress:

* We have implemented FOA-e to be executed in K8S,

* We have contributed to an Open-Source project called Kepler that measures the energy consumption of k8s components (containers, pods, nodes, etc).
* To improve FOA's **model** and try other linear programs' **solvers**,

* To study applications that can be modeled as **workflows** of serverless functions,

* To continue the investigations towards the reduction of energy consumption on serverless,

* To add both schedulers in the PHYSICS platforms,

* To include and use Kepler for a continuous measurement of the energy consumption,

* To perform experiments of k8s platforms to compare with the simulations and with default k8s scheduler.
# Towards an Energy-Aware Multi-objective Scheduling Policy for Server-based Edge-Cloud Continuum

Compas 2023, Annecy - France

## Towards a Multi-objective Scheduling Policy for Serverless-based Edge-Cloud Continuum

The 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing
Bangalore, India — May 1-4, 2023

L. Angelelli¹, Anderson Andrei Da Silva¹ ², Y. Georgiou², M. Mercier², G. Mounié¹, D. Trystram¹.

¹Université Grenoble Alpes, ²Ryax Technologies https://andersonandrei.github.io/
https://gitlab.com/andersonandrei/foa-a-multi-objective-scheduling-policy-for-serverless

<figure><img src="image_31.png" /></figure>

Reproducible
Artifacts

Thanks to the Intelligence of the Multi-disciplinary Institute on Artificial Intelligence MIAI at Grenoble Alpes (ANR-19-P3IA-0003),
to the European Union's Project H2020 PHYSICS (GA 101017047), and to the Grid'5000 testbed (https://www.grid5000.fr).