Abstract
Massive terminal users have brought explosive need of data residing at edge of overall network. Multiple Mobile Edge Computing (MEC) servers are built in/near base station to meet this need. However, optimal distribution of these servers to multiple users in real time is still a problem. Reinforcement Learning (RL) as a framework to solve interaction problem is a promising solution. In order to apply RL based algorithm into a multi-agent environment, we propose an iterative scheme: select individual users with priorities to interact with the environment iteratively one at a time Furthermore, we tried to optimize the overall system performance based on this scheme. Hence, we construct three objective system performance indicators: average processing cost, delay and energy consumption, improve the existing Deep Q-learning Network (DQN) by using the cost as reward function, changing the fixed exploitation rate into dynamic one that associated with reward and episode time. In order to explore the performance potential of the proposed algorithm, we have simulated the proposed algorithm, DQN algorithm and greedy algorithm under different users and data sizes. The results show that the proposed algorithm had reduced at least 12% of system average processing cost comparing to the greedy algorithm. It also outperform the greedy algorithm and DQN algorithm in delay and energy consumption significantly.
Original language | English |
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Title of host publication | Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022 |
Editors | C. Ceballos |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 25-32 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-6654-7315-6 |
ISBN (Print) | 978-1-6654-7316-3 |
DOIs | |
Publication status | Published - 2023 |
Event | 2022 IEEE 28th International Conference on Parallel and Distributed Systems - Nanjing, China Duration: 10 Jan 2023 → 12 Jan 2023 |
Publication series
Name | Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS |
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Volume | 2023-January |
ISSN (Print) | 1521-9097 |
Conference
Conference | 2022 IEEE 28th International Conference on Parallel and Distributed Systems |
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Abbreviated title | ICPADS 2022 |
Country/Territory | China |
City | Nanjing |
Period | 10/01/23 → 12/01/23 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Mobile Edge Computing
- Computation Offloading
- Reinforcement Learning
- Deep Q-Learning Network