EURO 2024 Copenhagen
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3982. A Robust Stochastic Approach to Edge Computing Service Network Design with Network Attacks Uncertainties

Invited abstract in session WC-34: Transportation and Logistics under Uncertainty, stream Stochastic, Robust and Distributionally Robust Optimization.

Wednesday, 12:30-14:00
Room: 43 (building: 303A)

Authors (first author is the speaker)

1. Jie Chen
College of Management and Economics, Tianjin University

Abstract

The Internet of Vehicles (IoV) serves as a foundational infrastructure for intelligent transportation systems. For service providers in the IoV and edge computing sectors, the meticulous design of edge computing networks is pivotal for securing a competitive advantage through enhanced cost efficiency and superior service quality. We introduce a robust stochastic model designed to tackle the network design challenge for edge servers effectively. This model incorporates critical considerations, such as the strategic placement of edge servers, capacity planning, congestion mitigation, accounting for stochastic demand, and guarding against unpredictable cyberattacks.Our primary objective is optimizing the network configuration to minimize total costs, including setup, capacity acquisition, congestion management, and data transmission. To represent this multifaceted issue accurately, we employ an M/M/1 queuing framework for assessing congestion in steady-state scenarios and refine our approach with a mixed-integer second-order cone programming framework.To manage uncertainties associated with network attacks, we incorporate an uncertainty set based on Renyi divergence, allowing us to adjust the model's conservativeness by modifying the divergence order. Furthermore, we propose a novel two-layer Benders decomposition approach to efficiently navigate the computational complexities inherent in solving this problem.

Keywords

Status: accepted

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