Reinforcement learning in the load balancing problem for the IFDAQ of the COMPASS experiment at CERN
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Reinforcement learning in the load balancing problem for the IFDAQ of the COMPASS experiment at CERN
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
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
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 12th International Conference on Agents and Artificial Intelligence
ISBN
978-989-758-395-7
ISSN
2184-433X
e-ISSN
—
Number of pages
8
Pages from-to
734-741
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Porto
Event location
Valletta
Event date
Feb 22, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000570769000080