Delay-Aware Robust Edge Network Hardening Under Decision-Dependent Uncertainty

Abstract

Edge computing promises to offer low-latency and ubiquitous computation to numerous devices at the network edge. For delay-sensitive applications, link delays significantly affect service quality. These delays can fluctuate substantially over time due to various factors such as network congestion, changing traffic conditions, cyberattacks, component failures, and natural disasters. Thus, it is crucial to efficiently harden the edge network to mitigate link delay variation and ensure a stable and improved user experience. To this end, we propose a novel robust model for optimal edge network hardening, considering link delay uncertainty. Unlike existing literature that treats uncertainties as exogenous, our model incorporates an endogenous uncertainty set to properly capture the impact of hardening and workload allocation decisions on link delays. However, the endogenous set introduces additional complexity to the problem due to the interdependence between decisions and uncertainties. To address this, we present two efficient methods to transform the problem into a solvable form. Extensive numerical results demonstrate the effectiveness of the proposed approach in mitigating delay variations and enhancing system performance.