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Task Offloading in ...
Task Offloading in Edge-cloud Computing using a Q-Learning Algorithm
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- Abdi, Somayeh (författare)
- Mälardalens universitet,Inbyggda system
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- Ashjaei, Seyed Mohammad Hossein, 1980- (författare)
- Mälardalens universitet,Inbyggda system
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- Mubeen, Saad (författare)
- Mälardalens universitet,Inbyggda system
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(creator_code:org_t)
- Science and Technology Publications, Lda, 2024
- 2024
- Engelska.
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Ingår i: International Conference on Cloud Computing and Services Science, CLOSER - Proceedings. - : Science and Technology Publications, Lda. - 9789897587016 ; , s. 159-166
- Relaterad länk:
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https://doi.org/10.5...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacityof fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines.This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximizethe number of independent IoT tasks that meet their deadlines and to minimize the deadline violationtime of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve theformulated problem. The performance of the proposed algorithms is experimentally evaluated with respect toseveral algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform wellin meeting task deadlines and reducing the total deadline violation time.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- Task Offloading
- Edge-Cloud Computing Continuum
- Reinforcement Learning
- Q-learning algorithm.
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