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Bill Estimation in Simplified Memory Progressive Second Price Auctions

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Economics of Grids, Clouds, Systems, and Services (GECON 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11819))

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Abstract

Vertical elasticity, the ability to add resources on-the-fly to a virtual machine or container, improves the aggregate benefit clients get from a given cloud hardware, namely the social welfare. To maximize the social welfare in vertical elasticity clouds, mechanisms which elicit resource valuation from clients are required. Full Vickrey-Clarke-Groves (VCG) auctions, which allocate resources to optimize the social welfare, are NP-hard and too computationally-complex for the task. However, VCG-like auctions, which have a reduced bidding language compared with VCG, are fast enough. Such is the Simplified Memory Progressive Second Price Auction (SMPSP). A key problem in VCG-like auctions is that they are not completely truthful, requiring participants, who wish to maximize their profits, to estimate their future bills. Bill estimation is particularly difficult since the bill is governed by other participants’ (changing) private bids.

We present methods to estimate future bills in noisy, changing, VCG-like auction environments. The bound estimation method we present leads to an increase of 3% in the overall social welfare.

Nir Lavi—The work was done while the author was a student at the Computer Science Dept., Technion.

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Notes

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Acknowledgments

This work was partially funded by the Amnon Pazi memorial research foundation, and supported by the Israeli Ministry of Science & Technology. We thank Orr Dunkelman for fruitful discussions. We also thank the Caesarea Rothschild Institute for Interdisciplinary Applications of Computer Science in the University of Haifa for their support. This research was also partially supported by the Center for Cyber, Law and Privacy and the Israel National Cyber Directorate.

Author information

Authors and Affiliations

  1. Computer Science Department, University of Haifa, Haifa, Israel

    Danielle Movsowitz-Davidow

  2. Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel

    Nir Lavi & Orna Agmon Ben-Yehuda

  3. Caesarea Rothschild Institute for Interdisciplinary Applications of Computer Science, University of Haifa, Haifa, Israel

    Orna Agmon Ben-Yehuda

Authors
  1. Danielle Movsowitz-Davidow
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  2. Nir Lavi
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  3. Orna Agmon Ben-Yehuda
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Corresponding authors

Correspondence to Danielle Movsowitz-Davidow or Orna Agmon Ben-Yehuda .

Editor information

Editors and Affiliations

  1. University of Leeds, Leeds, UK

    Karim Djemame

  2. Seoul National University, Seoul, Korea (Republic of)

    Jörn Altmann

  3. University of Zaragoza, Zaragoza, Spain

    José Ángel Bañares

  4. Technion – Israel Institute of Technology and University of Haifa, Haifa, Israel

    Orna Agmon Ben-Yehuda

  5. LUMSA University, Rome, Italy

    Maurizio Naldi

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Movsowitz-Davidow, D., Lavi, N., Agmon Ben-Yehuda, O. (2019). Bill Estimation in Simplified Memory Progressive Second Price Auctions. In: Djemame, K., Altmann, J., Bañares, J., Agmon Ben-Yehuda, O., Naldi, M. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2019. Lecture Notes in Computer Science(), vol 11819. Springer, Cham. https://doi.org/10.1007/978-3-030-36027-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-36027-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36026-9

  • Online ISBN: 978-3-030-36027-6

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