Reinforcement learning in the load balancing problem for the IFDAQ of the COMPASS experiment at CERN

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F20%3A00341113" target="_blank" >RIV/68407700:21340/20:00341113 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5220/0009035107340741" target="_blank" >https://doi.org/10.5220/0009035107340741</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0009035107340741" target="_blank" >10.5220/0009035107340741</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reinforcement learning in the load balancing problem for the IFDAQ of the COMPASS experiment at CERN

Popis výsledku v původním jazyce

Currently, modern experiments in high energy physics impose great demands on the reliability, efficiency, and data rate of Data Acquisition Systems (DAQ). The paper deals with the Load Balancing (LB) problem of the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN and presents a methodology applied in finding optimal solution. Machine learning approaches, seen as a subfield of artificial intelligence, have become crucial for many well-known optimization problems in recent years. Therefore, algorithms based on machine learning are worth investigating with respect to the LB problem. Reinforcement learning (RL) represents a machine learning search technique using an agent interacting with an environment so as to maximize certain notion of cumulative reward. In terms of RL, the LB problem is considered as a multi-stage decision making problem. Thus, the RL proposal consists of a learning algorithm using an adaptive ε-greedy strategy and a policy retrieval algorithm building a comprehensive search framework. Finally, the performance of the proposed RL approach is examined on two LB test cases and compared with other LB solution methods.

Název v anglickém jazyce

Reinforcement learning in the load balancing problem for the IFDAQ of the COMPASS experiment at CERN

Popis výsledku anglicky

Currently, modern experiments in high energy physics impose great demands on the reliability, efficiency, and data rate of Data Acquisition Systems (DAQ). The paper deals with the Load Balancing (LB) problem of the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN and presents a methodology applied in finding optimal solution. Machine learning approaches, seen as a subfield of artificial intelligence, have become crucial for many well-known optimization problems in recent years. Therefore, algorithms based on machine learning are worth investigating with respect to the LB problem. Reinforcement learning (RL) represents a machine learning search technique using an agent interacting with an environment so as to maximize certain notion of cumulative reward. In terms of RL, the LB problem is considered as a multi-stage decision making problem. Thus, the RL proposal consists of a learning algorithm using an adaptive ε-greedy strategy and a policy retrieval algorithm building a comprehensive search framework. Finally, the performance of the proposed RL approach is examined on two LB test cases and compared with other LB solution methods.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

ISBN

978-989-758-395-7

ISSN

2184-433X

e-ISSN

—

Počet stran výsledku

8

Strana od-do

734-741

Název nakladatele

SciTePress - Science and Technology Publications

Místo vydání

Porto

Místo konání akce

Valletta

Datum konání akce

22. 2. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000570769000080