A Truthful Online Mechanism for Resource Allocation in Fog Computing

Fan Bi, Sebastian Stein, Enrico Gerding, Nick Jennings, Thomas La Porta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationPRICAI 2019
Subtitle of host publicationTrends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsAbhaya C. Nayak, Alok Sharma
PublisherSpringer Verlag
Pages363-376
Number of pages14
ISBN (Print)9783030298937
DOIs
StatePublished - Jan 1 2019
Event16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019 - Yanuka Island, Fiji
Duration: Aug 26 2019Aug 30 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11672 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019
CountryFiji
CityYanuka Island
Period8/26/198/30/19

Fingerprint

Fog
Resource Allocation
Resource allocation
Internet of Things
Computing
Welfare
Resources
Optimal Allocation
Assign
Completion
Paradigm
Benchmark
Upper bound
Internet of things

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Bi, F., Stein, S., Gerding, E., Jennings, N., & La Porta, T. (2019). A Truthful Online Mechanism for Resource Allocation in Fog Computing. In A. C. Nayak, & A. Sharma (Eds.), PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 363-376). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11672 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-29894-4_30
Bi, Fan ; Stein, Sebastian ; Gerding, Enrico ; Jennings, Nick ; La Porta, Thomas. / A Truthful Online Mechanism for Resource Allocation in Fog Computing. PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. editor / Abhaya C. Nayak ; Alok Sharma. Springer Verlag, 2019. pp. 363-376 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Bi, F, Stein, S, Gerding, E, Jennings, N & La Porta, T 2019, A Truthful Online Mechanism for Resource Allocation in Fog Computing. in AC Nayak & A Sharma (eds), PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11672 LNAI, Springer Verlag, pp. 363-376, 16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019, Yanuka Island, Fiji, 8/26/19. https://doi.org/10.1007/978-3-030-29894-4_30

A Truthful Online Mechanism for Resource Allocation in Fog Computing. / Bi, Fan; Stein, Sebastian; Gerding, Enrico; Jennings, Nick; La Porta, Thomas.

PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. ed. / Abhaya C. Nayak; Alok Sharma. Springer Verlag, 2019. p. 363-376 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11672 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Bi F, Stein S, Gerding E, Jennings N, La Porta T. A Truthful Online Mechanism for Resource Allocation in Fog Computing. In Nayak AC, Sharma A, editors, PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2019. p. 363-376. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-29894-4_30