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COVID-19 Networking Demand: An Auction-Based Mechanism for Automated Selection of Edge Computing Services.

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The COVID-19 pandemic increased demand for remote connections, accelerating edge computing deployment. This study proposes an auction mechanism and decision-making model to help network brokers automate edge computing offer selection based on price and quality.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Operations Research

Background:

  • The COVID-19 pandemic significantly increased demand for remote connectivity and network services.
  • Network providers face challenges in meeting user demand due to the surge in traffic from stay-at-home orders.
  • The crisis accelerates the need for edge computing resources to manage distributed user traffic.

Purpose of the Study:

  • To address the challenge of selecting optimal edge computing resources amidst fluctuating prices and quality of service.
  • To propose a novel auction mechanism for network service brokers to automate edge computing offer selection.
  • To develop a multi-attribute decision-making model for brokers to maximize utility when evaluating multiple bids.

Main Methods:

  • Investigated multi-attribute decision-making problems in edge computing procurement.
  • Developed a novel auction mechanism for network service brokers.
  • Proposed a multi-attribute decision-making model to optimize broker utility.

Main Results:

  • The proposed auction mechanism automates the selection of edge computing offers.
  • The decision-making model enables brokers to maximize utility from diverse bids.
  • Evaluations demonstrate the practicality and robustness of the developed model.

Conclusions:

  • The proposed approach effectively addresses the complexities of edge computing resource selection for network brokers.
  • The automated selection process enhances efficiency and utility maximization in dynamic market conditions.
  • The study provides a robust solution for network providers navigating increased demand during crises.