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

Jiaming Cheng, Duong Thuy Anh Nguyen, Ni Trieu, Duong Tung Nguyen

TL;DR

A novel robust model for optimal edge network hardening, considering link delay uncertainty is proposed, which incorporates an endogenous uncertainty set to properly capture the impact of hardening and workload allocation decisions on link delays.

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.

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

TL;DR

A novel robust model for optimal edge network hardening, considering link delay uncertainty is proposed, which incorporates an endogenous uncertainty set to properly capture the impact of hardening and workload allocation decisions on link delays.

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.
Paper Structure (26 sections, 4 theorems, 54 equations, 8 figures, 4 tables)

This paper contains 26 sections, 4 theorems, 54 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Deterministic Hardening Model
  5. Uncertainty Modeling
  6. Solution Approach
  7. Overview of Solution Methods
  8. Robust Solution
  9. Enhanced Robust Solution
  10. Equivalence of RDDU and e-RDDU
  11. Comparison Between RDDU and e-RDDU
  12. Numerical Results
  13. Simulation Setting
  14. Sensitivity analysis
  15. Impacts of the link hardening costs and budget
  16. ...and 11 more sections

Key Result

Theorem 3.1

Constraint eq:MaxRobustConstOrg can be reformulated as: where $M$ is a sufficiently large constant.

Figures (8)

  • Figure 1: System model
  • Figure 2: Flowchart of solution approaches for the RO-DDU model. Two algorithms are shown: RDDU and e-RDDU.
  • Figure 3: Impacts of $\Psi$ and $B$ on the system performance
  • Figure 4: Impacts of the uncertainties on the system performance
  • Figure 5: Impacts of the uncertainty set
  • ...and 3 more figures

Theorems & Definitions (10)

  • Remark 2.1
  • Remark 2.2
  • Remark 2.3: On Linear Approximation of Network Delay
  • Theorem 3.1
  • proof
  • Theorem 3.2
  • proof
  • Remark 3.3: Computational Complexity
  • Theorem 1
  • Lemma 2