In this paper, we study an optimal service placement and workload allocation problem for a service provider (SP), who can procure resources from numerous edge nodes to serve its users.The SP aims to improve the user experience while minimizing its cost, considering various system uncertainties. To tackle this challenging problem, we propose a novel resilience-aware edge service placement and workload allocation model that jointly captures the uncertainties of resource demand and node failures. The first-stage decisions include the optimal service placement and resource procurement, while the optimal workload reallocation is determined in the second stage after the uncertainties are disclosed. The salient feature of the proposed model is that it produces a placement and procurement solution that is robust against any possible realization of the uncertainties. By leveraging the column-and-constraint generation method, we introduce two iterative algorithms that can converge to an exact optimal solution within a finite number of iterations. We further suggest an affine decision rule approximation approach for solving large-scale problem instances in a reasonable time. Extensive numerical results are shown to demonstrate the advantages of the proposed model and solutions.
翻译:在本文中,我们研究了服务提供者的最佳服务安置和工作量分配问题,服务提供者可以从多个边缘节点获取资源,为其用户服务。 SP旨在改进用户经验,同时尽量减少其成本,同时考虑到各种系统不确定性。为了解决这一具有挑战性的问题,我们提出了一个新的具有复原力的边缘服务安置和工作量分配模式,共同捕捉资源需求的不确定性和节点失灵。第一阶段的决定包括最佳服务安置和资源采购,而最佳工作量重新分配则在披露不确定性之后的第二阶段确定。拟议模式的突出特点是,它产生一种在任何可能的不确定性实现之前能够稳健的定位和采购解决方案。我们通过利用一栏和节点生成方法,引入了两种迭接法,能够在有限的迭代数内形成准确的最佳解决方案。我们进一步建议了在合理时间内解决大规模问题案例中的近似决定规则近似方法。我们展示了广泛的数字结果,以展示拟议模式和解决方案的优势。