决策依赖不确定性下延迟感知的鲁棒边缘网络加固
Delay-Aware Robust Edge Network Hardening Under Decision-Dependent Uncertainty
AI总结：
针对链路延迟不确定性下的边缘网络加固问题，提出一种引入内生不确定性集的鲁棒优化模型，并给出两种方法将问题转化为可解形式，有效减轻延迟波动并提升用户体验。
摘要：
边缘计算有望为网络边缘的大量设备提供低延迟和无处不在的计算。对于延迟敏感的应用，链路延迟会直接影响服务质量。由于网络拥塞、不断变化的流量状况、网络攻击、组件故障和自然灾害等各种因素，这些延迟会随时间剧烈波动。因此，高效地加固边缘网络以减轻链路延迟变化并确保稳定且改善的用户体验至关重要。为此，我们提出了一种新颖的鲁棒模型，用于在考虑链路延迟不确定性的情况下进行最优边缘网络加固。与现有将不确定性视为外生的文献不同，我们的模型引入了内生不确定性集，以恰当地捕获加固和工作负载分配决策对链路延迟的影响。然而，由于决策与不确定性之间的相互依赖性，内生集给问题引入了额外的复杂性。我们提出了两种有效的方法，将问题转化为可解的形式。广泛的数值结果展示了所提方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
