Edge computing has emerged as a key technology to reduce network traffic, improve user experience, and enable various Internet of Things applications. From the perspective of a service provider (SP), how to jointly optimize the service placement, sizing, and workload allocation decisions is an important and challenging problem, which becomes even more complicated when considering demand uncertainty. To this end, we propose a novel two-stage adaptive robust optimization framework to help the SP optimally determine the locations for installing their service (i.e., placement) and the amount of computing resource to purchase from each location (i.e., sizing). The service placement and sizing solution of the proposed model can hedge against any possible realization within the uncertainty set of traffic demand. Given the first-stage robust solution, the optimal resource and workload allocation decisions are computed in the second-stage after the uncertainty is revealed. To solve the two-stage model, in this paper, we present an iterative solution by employing the column-and-constraint generation method that decomposes the underlying problem into a master problem and a max-min subproblem associated with the second stage. Extensive numerical results are shown to illustrate the effectiveness of the proposed two-stage robust optimization model.
翻译:电磁计算已成为减少网络流量、改善用户经验和促进各种物联网应用的关键技术。从服务提供者(SP)的角度来看,如何共同优化服务配置、规模化和工作量分配决定是一个重要而具有挑战性的问题,在考虑需求不确定性时,这个问题甚至变得更加复杂。为此,我们提出一个新的两阶段适应性强强的优化框架,以帮助SP最佳地确定安装服务的地点(即职位安排)和从每个地点购买的计算资源的数量(即规模化 ) 。拟议模式的服务安置和规模化解决方案可以避免在交通需求的不确定性范围内实现任何可能的实现。考虑到第一阶段的强有力解决方案,在不确定性暴露后,在第二阶段计算最佳资源和工作量分配决定。为了解决两阶段模式,我们在本文件中提出一个迭代解决方案,即采用将根本问题分解成一个主问题和与第二阶段相关的最大分错位法。广泛的数字结果可以说明拟议的两阶段模式的可靠优化。