DRL-based dependent task offloading with delay-energy tradeoff in medical image edge computing Article Swipe
Qi Liu
,
Zhao Tian
,
Ning Wang
,
Yusong Lin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-023-01322-x
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-023-01322-x
Task offloading solves the problem that the computing resources of terminal devices in hospitals are limited by offloading massive radiomics-based medical image diagnosis model (RIDM) tasks to edge servers (ESs). However, sequential offloading decision-making is NP-hard. Representing the dependencies of tasks and developing collaborative computing between ESs have become challenges. In addition, model-free deep reinforcement learning (DRL) has poor sample efficiency and brittleness to hyperparameters. To address these challenges, we propose a distributed collaborative dependent task offloading strategy based on DRL (DCDO-DRL). The objective is to maximize the utility of RIDM tasks, which is a weighted sum of the delay and energy consumption generated by execution. The dependencies of the RIDM task are modeled as a directed acyclic graph (DAG). The sequence prediction of the S2S neural network is adopted to represent the offloading decision process within the DAG. Next, a distributed collaborative processing algorithm is designed on the edge layer to further improve run efficiency. Finally, the DCDO-DRL strategy follows the discrete soft actor-critic method to improve the robustness of the S2S neural network. The numerical results prove the convergence and statistical superiority of the DCDO-DRL strategy. Compared with other algorithms, the DCDO-DRL strategy improves the execution utility of the RIDM task by at least 23.07, 12.77, and 8.51% in the three scenarios.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40747-023-01322-x
- https://link.springer.com/content/pdf/10.1007/s40747-023-01322-x.pdf
- OA Status
- gold
- Cited By
- 12
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391296275
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391296275Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s40747-023-01322-xDigital Object Identifier
- Title
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DRL-based dependent task offloading with delay-energy tradeoff in medical image edge computingWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-29Full publication date if available
- Authors
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Qi Liu, Zhao Tian, Ning Wang, Yusong LinList of authors in order
- Landing page
-
https://doi.org/10.1007/s40747-023-01322-xPublisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s40747-023-01322-x.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s40747-023-01322-x.pdfDirect OA link when available
- Concepts
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Computational intelligence, Task (project management), Computer science, Image (mathematics), Edge computing, Enhanced Data Rates for GSM Evolution, Energy (signal processing), Artificial intelligence, Mathematics, Engineering, Systems engineering, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
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2025: 9, 2024: 3Per-year citation counts (last 5 years)
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64Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s40747-023-01322-x.pdf |
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| primary_location.raw_source_name | Complex & Intelligent Systems |
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| publication_date | 2024-01-29 |
| publication_year | 2024 |
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