A truthful online mechanism for resource allocation in fog computing
File(s)
Accepted version
Author(s)
Bi, Fan
Stein, Sebastian
Gerding, Enrico
Jennings, Nick
La Porta, Thomas
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.
Date Issued
2019-08-23
Date Acceptance
2019-08-01
Citation
PRICAI 2019: Trends in Artificial Intelligence, 2019, 3, pp.363-376
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.
Source
Pacific Rim International Conference on Artificial Intelligence (PRICAI)
Subjects
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2019-08-26
Finish Date
2019-08-30
Coverage Spatial
Cuvu, Yanuka Island, Fiji
Date Publish Online
2019-08-23