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Abstract
An observable trend in recent years is the increasing demand for more complex services designed to be used with portable or automotive embedded devices. The problem is that these devices may lack the computational resources necessary to comply with service requirements. To solve it, cloud and edge computing, and in particular, the recent multi-access edge computing (MEC) paradigm, have been proposed. By offloading the processing of computational tasks from devices or vehicles to an external network, a larger amount of computational resources, placed in different locations, becomes accessible. However, this in turn creates the issue of deciding where each task should be executed. In this paper, we model the problem of computation offloading of vehicular applications to solve it using deep reinforcement learning (DRL) and evaluate the performance of different DRL algorithms and heuristics, showing the advantages of the former methods. Moreover, the impact of two scheduling techniques in computing nodes and two reward strategies in the DRL methods are also analyzed and discussed.
Original language | English |
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Title of host publication | 2022 International Balkan Conference on Communications and Networking, BalkanCom 2022 |
Number of pages | 5 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Publication date | 29 Sept 2022 |
Pages | 31-35 |
ISBN (Print) | 978-1-6654-8765-8 |
ISBN (Electronic) | 978-1-6654-8764-1 |
DOIs | |
Publication status | Published - 29 Sept 2022 |
Event | 2022 International Balkan Conference on Communications and Networking, BalkanCom 2022 - Sarajevo, Bosnia and Herzegovina Duration: 22 Aug 2022 → 24 Aug 2022 |
Conference
Conference | 2022 International Balkan Conference on Communications and Networking, BalkanCom 2022 |
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Country/Territory | Bosnia and Herzegovina |
City | Sarajevo |
Period | 22/08/2022 → 24/08/2022 |
Bibliographical note
Funding Information:This work has been supported by Consejería de Educación de la Junta de Castilla y León and the European Regional Development Fund (Grant VA231P20), and the Spanish Ministry of Science of Innovation and the State Research Agency (Grant PID2020-112675RB-C42 funded by MCIN/AEI/10.13039/501100011033 and RED2018-102585-T). This work has also received funding from the European Union Horizon 2020 research and innovation programme under the grant agreement No 856967.
Publisher Copyright:
© 2022 IEEE.
Keywords
- Computation Offloading
- Deep Reinforcement Learning
- Edge Computing
- Vehicular Applications
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Deep Reinforcement Learning Applied to Computational Offloading of Vehicular Applications: A Comparison
Ferens Michalek, M. J. (Speaker)
22 Aug 2022Activity: Talks and presentations › Conference presentations