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Resiliency plays a critical role in designing future communication networks.How to make edge computing systems resilient against unpredictable failures andfluctuating demand is an important and challenging problem. To this end, thispaper investigates a resilient service placement and workload allocationproblem for a service provider (SP) who can procure resources from numerousedge nodes to serve its users, considering both resource demand and nodefailure uncertainties. We introduce a novel two-stage adaptive robust model tocapture this problem. The service placement and resource procurement decisionsare optimized in the first stage while the workload allocation decision isdetermined in the second stage after the uncertainty realization. By exploitingthe special structure of the uncertainty set, we develop an efficient iterativealgorithm that can converge to an exact optimal solution within a finite numberof iterations. We further present an affine decision rule approximationapproach for solving large-scale problem instances in a reasonable time.Extensive numerical results demonstrate the advantages of the proposed modeland approaches, which can help the SP make proactive decisions to mitigate theimpacts of the uncertainties.

Resilient Edge Service Placement under Demand and Node Failure Uncertainties