Collaborative Coded Computation Offloading: An All-pay Auction Approach Article Swipe
Jer Shyuan Ng
,
Wei Yang Bryan Lim
,
Sahil Garg
,
Zehui Xiong
,
Dusit Niyato
,
Mohsen Guizani
,
Cyril Leung
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/icc42927.2021.9500308
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/icc42927.2021.9500308
As the amount of data collected for crowdsensing applications increases rapidly due to improved sensing capabilities and the increasing number of Internet of Things (IoT) devices, the cloud server is no longer able to handle the large-scale datasets individually. Given the improved computational capabilities of the edge devices, coded distributed computing has become a promising approach given that it allows computation tasks to be carried out in a distributed manner while mitigating straggler effects, which often account for the long overall completion times. Specifically, by using polynomial codes, computed results from only a subset of devices are needed to reconstruct the final result. However, there is no incentive for the edge devices to complete the computation tasks. In this paper, we present an all-pay auction to incentivize the edge devices to participate in the coded computation tasks. In this auction, the bids of the edge devices are represented by the allocation of their Central Processing Unit (CPU) power to the computation tasks. All edge devices submit their bids regardless of whether they win or lose in the auction. The all-pay auction is designed to maximize the utility of the cloud server by determining the reward allocation to the winners. Simulation results show that the edge devices are incentivized to allocate more CPU power when multiple rewards are offered instead of a single reward.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/icc42927.2021.9500308
- OA Status
- green
- Cited By
- 3
- References
- 19
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3111354771
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3111354771Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icc42927.2021.9500308Digital Object Identifier
- Title
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Collaborative Coded Computation Offloading: An All-pay Auction ApproachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-01Full publication date if available
- Authors
-
Jer Shyuan Ng, Wei Yang Bryan Lim, Sahil Garg, Zehui Xiong, Dusit Niyato, Mohsen Guizani, Cyril LeungList of authors in order
- Landing page
-
https://doi.org/10.1109/icc42927.2021.9500308Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2012.04854Direct OA link when available
- Concepts
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Computer science, Computation, Computation offloading, Cloud computing, Enhanced Data Rates for GSM Evolution, Edge computing, Central processing unit, Edge device, Distributed computing, Operating system, Algorithm, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
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19Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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