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Real-time Pricing-based Resource Allocation in Open Market Environments

Published: 05 April 2023 Publication History

Abstract

Open market environments consist of a set of participants (vendors and consumers) that dynamically leave or join the market. As a result, the arising dynamism leads to uncertainties in supply and demand of the resources in these open markets. In specific, in such uncertain markets, vendors attempt to maximise their revenue by dynamically changing their selling prices according to the market demand. In this regard, an optimal resource allocation approach becomes immensely needed to optimise the selling prices based on the supply and demand of the resources in the open market. Therefore, optimal selling prices should maximise the revenue of vendors while protecting the utility of buyers. In this context, we propose a real-time pricing approach for resource allocation in open market environments. The proposed approach introduces a priority-based fairness mechanism to allocate the available resources in a reverse-auction paradigm. Finally, we compare the proposed approach with two state-of-the-art resource allocation approaches. The experimental results show that the proposed approach outperforms the other two resource allocation approaches in its ability to maximise the vendors’ revenue.

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Published In

cover image ACM Transactions on Internet Technology
ACM Transactions on Internet Technology  Volume 23, Issue 1
February 2023
564 pages
ISSN:1533-5399
EISSN:1557-6051
DOI:10.1145/3584863
  • Editor:
  • Ling Liu
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 April 2023
Online AM: 14 March 2023
Accepted: 06 May 2021
Revised: 09 March 2021
Received: 15 July 2020
Published in TOIT Volume 23, Issue 1

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Author Tags

  1. Open market environments
  2. resource allocation
  3. reinforcement learning
  4. real-time pricing

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  • Joint Degree Program (JDP)
  • KAKENHI Young Researcher

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