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

TL;DR

This paper introduces a robust optimization model for edge network hardening that accounts for delay uncertainties influenced by network decisions, providing effective solutions for delay-sensitive applications.

Contribution

It develops a novel endogenous uncertainty set model for edge network hardening and proposes two methods to solve the complex optimization problem.

Findings

The proposed model effectively mitigates delay variations.

The methods improve solution efficiency for complex uncertainty sets.

Numerical results validate the approach's robustness and practicality.

Abstract

Edge computing promises to offer low-latency and ubiquitous computation to numerous devices at the network edge. For delay-sensitive applications, link delays can have a direct impact on service quality. These delays can fluctuate drastically 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 as well as ensure a stable and improved user experience. To this end, we propose a novel robust model for optimal edge network hardening, considering the link delay uncertainty. Departing from the 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. We present two efficient methods to transform the problem into a solvable form. Extensive numerical results are shown to demonstrate the effectiveness of the proposed approach.