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A Unified Comparative Study of Heuristic Algorithms for Double Combinatorial Auctions: Locality-Constrained Resource Allocation Problems

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Agents and Artificial Intelligence (ICAART 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11352))

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Abstract

Market-oriented resource allocation in cloud computing is driven by increasingly stringent needs for flexibility, fine-grained allocation, and more critically, revenue maximization. Double combinatorial auctions aptly address these demands, but their \(\mathcal {NP}\)-hardness has hindered them from being widely adopted. Heuristic algorithms, with their input-dependent performance and solution quality, have failed to offer a robust alternative. We posit that a unifying approach for evaluating all existing algorithms, under the umbrella of a consistent problem formulation and a variety of common test cases, can propel combinatorial auctions towards real-world usage.

In this paper, we performed an extensive empirical evaluation of a portfolio of heuristic algorithms for double combinatorial auctions, applied to problems with hard resource locality constraints. We found that there is no single algorithm that outperforms the others in all test scenarios. However, we offer insights into the behavior of the algorithms, and provide methods to explore the portfolio’s performance over a wide range of input scenarios.

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Authors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Diana Gudu, Marcus Hardt & Achim Streit

Authors
  1. Diana Gudu
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  2. Marcus Hardt
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  3. Achim Streit
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Corresponding author

Correspondence to Diana Gudu .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  2. University of Porto, Porto, Portugal

    Ana Paula Rocha

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Gudu, D., Hardt, M., Streit, A. (2019). A Unified Comparative Study of Heuristic Algorithms for Double Combinatorial Auctions: Locality-Constrained Resource Allocation Problems. In: van den Herik, J., Rocha, A. (eds) Agents and Artificial Intelligence. ICAART 2018. Lecture Notes in Computer Science(), vol 11352. Springer, Cham. https://doi.org/10.1007/978-3-030-05453-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-05453-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05452-6

  • Online ISBN: 978-3-030-05453-3

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