A truthful online mechanism for resource allocation in fog computing

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Title: A truthful online mechanism for resource allocation in fog computing
Authors: Bi, F
Stein, S
Gerding, E
Jennings, N
La Porta, T
Item Type: Conference Paper
Abstract: Fog computing is a promising Internet of Things (IoT) paradigm in which data is processed near its source. Here, efficient resource allocation mechanisms are needed to assign limited fog resources to competing IoT tasks. To this end, we consider two challenges: (1) near-optimal resource allocation in a fog computing system; (2) incentivising self-interested fog users to report their tasks truthfully. To address these challenges, we develop a truthful online resource allocation mechanism called flexible online greedy. The key idea is that the mechanism only commits a certain amount of computational resources to a task when it arrives. However, when and where to allocate resources stays flexible until the completion of the task. We compare our mechanism to four benchmarks and show that it outperforms all of them in terms of social welfare by up to 10% and achieves a social welfare of about 90% of the offline optimal upper bound.
Issue Date: 23-Aug-2019
Date of Acceptance: 1-Aug-2019
URI: http://hdl.handle.net/10044/1/74144
DOI: https://doi.org/10.1007/978-3-030-29894-4_30
ISBN: 9783030298937
ISSN: 0302-9743
Publisher: Springer International Publishing
Start Page: 363
End Page: 376
Journal / Book Title: PRICAI 2019: Trends in Artificial Intelligence
Volume: 3
Copyright Statement: © 2019 Springer Nature Switzerland AG.
Conference Name: Pacific Rim International Conference on Artificial Intelligence (PRICAI)
Keywords: Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2019-08-26
Finish Date: 2019-08-30
Conference Place: Cuvu, Yanuka Island, Fiji
Embargo Date: 2020-08-23
Open Access location: https://link.springer.com/chapter/10.1007/978-3-030-29894-4_30
Online Publication Date: 2019-08-23
Appears in Collections:Computing
Faculty of Natural Sciences



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