需求与节点失效不确定性下的弹性边缘服务部署
Resilient Edge Service Placement under Demand and Node Failure Uncertainties
AI总结：
本文针对资源需求与节点失效不确定性，提出两阶段自适应鲁棒模型优化边缘服务部署与工作负载分配，并开发高效迭代算法与仿射决策规则近似方法求解，帮助服务提供商主动减轻不确定性影响。
摘要：
弹性在未来通信网络设计中起着关键作用。如何使边缘计算系统对不可预测的失效和需求波动具有弹性，是一个重要且具有挑战性的问题。为此，本文研究了一个弹性服务部署与工作负载分配问题，针对一个可以从众多边缘节点采购资源以服务其用户的服务提供商（SP），同时考虑资源需求和节点失效的不确定性。我们引入了一个新颖的两阶段自适应鲁棒模型来刻画此问题。服务部署与资源采购决策在第一阶段优化，而工作负载分配决策在不确定性实现后的第二阶段确定。通过利用不确定性集的特殊结构，我们开发了一种高效的迭代算法，能在有限次迭代内收敛到精确最优解。我们进一步提出了一种仿射决策规则近似方法，以在合理时间内求解大规模问题实例。大量数值结果证明了所提出模型和方法的优越性，可帮助SP做出主动决策以减轻不确定性的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
