Robust Optimization for Resource Allocation in Cloud Providers

三篇论文(一篇为第一作者)已被高级别会议接受；一篇文章(第一作者)已发表在一本顶级期刊上。已经提交了三篇期刊论文。这些工作集中于不同应用中的资源分配问题，包括云计算和网络功能虚拟化(NFV)，它们通常存在网络故障和流量不确定性，从而降低网络性能。对计算资源提供概率保护，减少所需的计算能力。分析了多故障情况下的备份传输资源共享情况，计算了备份传输所需的最小容量。通过我们的分析，网络运营商可以根据实际需要设置适当的备份传输资源共享程度。在未来的扩展中，我们计划对计算资源和传输资源应用概率保护，以进一步减少所需的网络资源。另一项工作引入了稳健的优化模型来处理NFV中服务部署的流量不确定性。它提供了不同的方法来解决部署问题。在此基础上，网络运营商可以以经济高效的方式开发针对流量不确定性的服务。在未来的工作中，我们计划解决一个更准确的模型，以进一步降低当前模型中保守近似引入的部署成本。

Three articles (one as the 1st author) have been accepted by high-level conferences; one article (1st author) has been published at a top-level journal. Three journal articles have been submitted. These works focused on resource allocation problems in different applications; it includes cloud computing and network function virtualization (NFV), where network failures and traffic uncertainty typically exist, which degrade the network performance.One work developed a backup computing and transmission resource allocation model against multiple node failures. Probabilistic protection is provided for computing resource to reduce the required computing capacity. It analyzed backup transmission resource sharing in the case of multiple failures to compute the minimum required backup transmission capacity. With our analyses, a network operator can set an appropriate degree of backup transmission resource sharing based on practical requirements. For future extensions, we plan to apply probabilistic protection for both computing and transmission resources to further reduce the required network resources.Another work introduced a robust optimization model to handle the traffic uncertainty for service deployment in NFV. It provided different approaches to solve the deployment problem. Based on it, a network operator can develop services against traffic uncertainty in a cost-efficient way. For future work, we plan to address a more accurate model to further reduce the deployment cost introduced by conservative approximation in the current one.