Distributed Online Learning of Fog Computing Under Nonuniform Device Cardinality
Abstract
Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the a-priori knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.
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Communication efficiency of a client largely depends on local devices' processing abilities and their geographical distributions in mobile edge computing systems.
“Moreover, the communication efficiency of a client largely depends on the local devices' processing abilities and their geographical distributions in mobile edge computing systems [161].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [161]
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“Moreover, the communication efficiency of a client largely depends on the local devices’ processing abilities and their geographical distributions in mobile edge computing systems [161].”
BackgroundSelf-citation
- Authors
- Ren, C., Lyu, X., Ni, W., Tian, H., & Liu, R. P.
- Affiliations
- University of Technology Sydney (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Beijing University of Posts and Telecommunications (CN)
- Venue
- IEEE Internet of Things Journal
- Published
- 2019-02
- Volume
- 6
- Issue
- 1
- Pages
- 1147-1159
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2889306979
- Cited by
- 22
- References
- 30
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 4 · 2021: 1 · 2020: 10 · 2019: 3
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61790553, 61421061)
- National Key Research and Development Program of China (2017ZX03001003)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Age of Information Optimization
- Keywords
- fog computing, distributed online learning, Internet of Things, stochastic gradient descent, graph matching, linearization, edge servers, energy efficiency, throughput
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