Resiliency plays a critical role in designing future communication networks. How to make edge computing systems resilient against unpredictable failures and fluctuating demand is an important and challenging problem. To this end, this paper investigates a resilient service placement and workload allocation problem for a service provider (SP) who can procure resources from numerous edge nodes to serve its users, considering both resource demand and node failure uncertainties. We introduce a novel two-stage adaptive robust model to capture this problem. The service placement and resource procurement decisions are optimized in the first stage while the workload allocation decision is determined in the second stage after the uncertainty realization. By exploiting the special structure of the uncertainty set, we develop an efficient iterative algorithm that can converge to an exact optimal solution within a finite number of iterations. We further present an affine decision rule approximation approach for solving large-scale problem instances in a reasonable time. Extensive numerical results demonstrate the advantages of the proposed model and approaches, which can help the SP make proactive decisions to mitigate the impacts of the uncertainties.

翻译：弹性在面向未来通信网络设计中具有关键作用。如何使边缘计算系统在不可预测的故障与波动需求下保持弹性，是一项重要且具有挑战性的问题。为此，本文研究了一个服务提供商在资源需求与节点故障不确定性下，从众多边缘节点采购资源以服务用户时的弹性服务部署与工作负载分配问题。我们引入了一种新颖的两阶段自适应鲁棒模型来刻画该问题：第一阶段优化服务部署与资源采购决策，第二阶段在不确定性实现后确定工作负载分配决策。通过利用不确定性集合的特殊结构，我们开发了一种高效的迭代算法，该算法能在有限迭代次数内收敛至精确最优解。进一步，我们提出了一种仿射决策规则近似方法，用于在合理时间内求解大规模问题实例。大量数值实验结果表明，所提出的模型与方法具有显著优势，能够帮助服务商做出主动决策以减轻不确定性带来的影响。