  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.
