Two-Stage Distributionally Robust Edge Node Placement Under Endogenous Demand Uncertainty

• Published in: IEEE INFOCOM 2024 - IEEE Conference on Computer Communications pp. 2388 - 2397
• Main Authors: Cheng, Jiaming, Anh Nguyen, Duong Thuy, Tung Nguyen, Duong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20-05-2024
• Subjects: Computational efficiency; Computational modeling; decision-dependent uncertainty; distributionally robust optimization; Edge computing; edge node placement; Numerical models; Resource management; Ubiquitous computing; Uncertainty; User experience
• ISSN: 2641-9874
• DOI: 10.1109/INFOCOM52122.2024.10621372

Edge computing (EC) promises to deliver low-latency and ubiquitous computation to numerous devices at the network edge. This paper aims to jointly optimize edge node (EN) placement and resource allocation for an EC platform, considering demand uncertainty. Diverging from existing approaches treating uncertainties as exogenous, we propose a novel two-stage decision-dependent distributionally robust optimization (DRO) framework to effectively capture the interdependence between EN placement decisions and uncertain demands. The first stage involves making EN placement decisions, while the second stage optimizes resource allocation after uncertainty revelation. We present an exact mixed-integer linear program reformulation for solving the underlying "min-max-min" two-stage model. We further introduce a valid inequality method to enhance computational efficiency, especially for large-scale networks. Extensive numerical experiments demonstrate the benefits of considering endogenous uncertainties and the advantages of the proposed model and approach.