Worst-Case Attacks in Reactive Edge-Cloud Systems: Expected Latency and SLA Violation

Abstract

Edge-cloud systems are increasingly deployed to meet the demands of real-time applications by processing latency-sensitive tasks at the edge site, near the end-devices. When load exceeds capacity, offloading of requests to the cloud allows the system to balance the spike. As their popularity increases, edge servers become an attractive target for attackers whose goal is to degrade system performance.This paper investigates worst-case (i.e., damage-maximizing) attacks on reactive edge-cloud systems, focusing on two key performance metrics: expected end-to-end latency and the probability of violating service-level agreements (SLAs). We use a general modeling framework based on two layers of computation and analyze how adversarial removal of edge servers affects delay under reactive offloading mitigation.We adopt common queueing models and prove that the system’s performance remain convex in the attack size, even when accounting for reactive offloading by the edge. This enables a characterization of worst-case policies as concentrated attacks, targeting only a few critical sites to maximize damage, yielding greater performance degradation than spreading efforts across the network. Simulation results validate the analysis and demonstrate that the identified worst-case attack strategies can increase damage by up to 40% using the same attack resources.