# Composition of Scheduling and Control Theory Techniques

Raphaël Bleuse, Éric Rutten

► To cite this version:

Raphaël Bleuse, Éric Rutten. Composition of Scheduling and Control Theory Techniques. JLESC 2023 - 15th Workshop Joint Laboratory for Extreme Scale Computing, Mar 2023, Bordeaux, France. pp.1-10. hal-04050732

HAL Id: hal-04050732

https://inria.hal.science/hal-04050732

Submitted on 29 Mar 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Composition of
Scheduling and Control Theory
Techniques

Raphaël BLEUSE, Éric RUTTEN

Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France

15th JLESC workshop
2023-03-22
Scheduling (~ Resource Allocation)

<figure><img src="image_2.png" /></figure>

platform

tasks/jobs

objective

⇒ where? & when?
# Scheduling (~ Resource Allocation)

<figure><img src="image_3.png" /></figure>

## But simplistic models

Many sources of *extrinsic variability*:

* Offline hardware spec., aging, ...

* Online phases (e.g., I/O, compute), failures, temperature, ...

platform

tasks/jobs

objective

⇒ where? & when?
# Feedback loops (à la Control Theory)

## Autonomic Computing (Kephart et al. 2003)

1. monitor system state

2. choose (online) parameters

3. repeat

<figure><img src="image_4.png" /></figure>

## Control Theory

### Challenges

- stability, accuracy, explainability

- transient vs. steady-state behavior

### Gains

- simpler models

- adapt to actual behavior
# Unexploited loops

Most scheduling approaches rely on an *unrealist* open-loop model. They struggle/fail to catch for example:

- congestion

- user behavior (Feitelson 2016)

<figure><img src="image_5.png" /></figure>

Classic workarounds: “cyclic” scheduling, more variables (increases models’ complexity), ...
Existing usages/compositions: RAPL×NRM

(Cerf et al. 2021; Hawila et al. 2022), ongoing collab. ANL/Inria
**Objective** Reducing energy consumption while sustaining performance
**Approach** Dynamic power regulation using Control Theory

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
# Existing usages/compositions: Cigri

## (Guilloteau et al. 2022)

**Objective** Minimize wasted resources (idle node, killed BE jobs)

**Constraint** No (low?) impact on local users

<figure><img src="image_9.png" /></figure>
# Potential combinations

Scheduling × Control Theory

<figure><img src="image_10.png" /></figure>

## Open questions / Directions to explore

- what are the interesting properties to enforce?

- how to structure the loops?

- what to measure? what to control?

- what is the dynamic at play?
<figure><img src="image_11.png" /></figure>

Backup slides
# Control Theory Methodology

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
References I

S. Cerf et al., "Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach," in Euro-par, Vol. 12820, Lecture Notes in Computer Science (2021), pp. 334-349.

D. G. Feitelson, "Resampling with Feedback, A New Paradigm of Using Workload Data for Performance Evaluation," in Euro-par, Vol. 9833, Lecture Notes in Computer Science (Aug. 2016), pp. 3-21.

Q. Guilloteau, B. Robu, C. Join, M. Fliess, É. Rutten, and O. Richard, "Model-Free Control for Resource Harvesting in Computing Grids," in CCTA (Aug. 2022), pp. 384-390.

I. Hawila, S. Cerf, R. Bleuse, S. Perarnau, and É. Rutten, "Adaptive Power Control for Sober High-Performance Computing," in CCTA (Aug. 2022), pp. 403-410.

J. O. Kephart and D. M. Chess, "The Vision of Autonomic Computing," IEEE Computer 36, 41-50 (2003).