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Rational Social Edge Computing

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Social Edge Computing

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Abstract

With the ever-increasing computing power of edge devices and the growing acceptance of running human-centric learning and intelligence applications on such edge systems, effectively allocating computation tasks to the edge devices has emerged as a critical undertaking for maximizing the performance of SEC systems. Task allocation in SEC faces several unique challenges (e.g., conflicting interests, constrained cooperativeness, dynamic compliance) that are centered around the “rational actor” nature of edge devices. To overcome these challenges, this chapter reviews a novel game-theoretic task allocation framework: Cooperative-Competitive Game-theoretic Task Allocation (CoGTA). The CoGTA framework includes a dynamic feedback incentive scheme, a decentralized fictitious play design with a new negotiation scheme, and a judiciously designed private payoff function.

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Notes

  1. 1.

    Note that in the real-time system community, tasks are periodically initialized while jobs are instances of tasks. We choose to follow the nomenclature used in the distributed system community for the words job and task, i.e., jobs are entities containing tasks. Thus, we say that jobs (and the tasks in the jobs) are periodically initialized.

  2. 2.

    The CoGTA framework can be readily extended to more complicated energy models, e.g., supporting multiple voltage/frequency levels. The details are omitted due to the page limit.

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Authors and Affiliations

  1. Champaign, IL, USA

    Dong Wang

  2. Amazon Alexa AI, Seattle, WA, USA

    Daniel ‘Yue’ Zhang

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  1. Dong Wang
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  2. Daniel ‘Yue’ Zhang
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Wang, D., Zhang, D.‘. (2023). Rational Social Edge Computing. In: Social Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26936-3_3

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  • DOI: https://doi.org/10.1007/978-3-031-26936-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26935-6

  • Online ISBN: 978-3-031-26936-3

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