Collaborative Research: CNS Core: Small: Dynamic Pricing and Procurement for Distributed Networked Platforms

如今，许多行业都以某种网络平台为特征，消费者可以在其中从供应商网络购买资源。例如，移动边缘用户可以组成网络并将其计算资源出租给消费者。然而，为了让这些新兴企业生存和发展，它们应该确保它们的分布式资源得到合理的定价，并以适当的数量提供给用户。否则，一些提供商可能会发现自己的用户过多，无法满足他们的需求。卡内基梅隆大学（CMU）和马萨诸塞大学阿默斯特分校（UMass）之间的这个合作项目旨在设计基本的定价、采购和调度策略，以确保用户在整个平台上分布良好，并将这些策略应用于边缘计算的新兴应用。最优的动态定价方案可以向用户发出信号，哪些供应商有可用的资源，而相反，动态采购允许网络化供应商根据用户需求调整其资源。调度方案通过利用用户需求的时间灵活性来最好地为用户分配资源，从而补充定价和采购解决方案。虽然已有一些研究分别考虑了网络平台的最优定价和调度策略，但本项目首次为不确定条件下的动态定价/采购和调度联合制定提出了基础理论。本项目将开发具有理论性能保证的定价、采购和调度算法；将这些解决方案与基于学习的方法相结合，以管理鲁棒性和性能之间的权衡；并在边缘计算场景中验证这些解决方案。拟议的定价、采购和调度解决方案的成功开发将使边缘计算业务更具利润和竞争力。提供商可以深入了解如何对其资源进行最佳定价，而用户可以获得灵活性，从而有助于降低满足其需求的成本。此外，所开发的理论工具将为在线优化和学习研究做出基础贡献。除了这些更广泛的技术影响外，该项目还将支持若干教育和外联活动。这些项目包括本科生研究项目、将研究成果纳入参与院校的课程，以及在研讨会上发表演讲和互动会议，以扩大对计算机的参与。这个项目的结果将保存在一个在线存储库中，由UMass或CMU托管。这些预计将包括研究成果的技术报告，设计算法的软件原型，以及为边缘计算实验收集的数据集和实验结果。存储库中的材料在项目结束后至少两年内仍可使用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many industries today feature some kind of networked platform, where consumers may purchase resources from a network of providers. For example, mobile edge users can form a network and rent their compute resources to consumers. In order for these emerging businesses to survive and grow, however, they should ensure that their distributed resources are priced properly and made available in the proper amounts to users. Otherwise, some providers may find themselves overloaded with users and unable to serve their demands. This collaborative project between Carnegie Mellon University (CMU) and the University of Massachusetts Amherst (UMass) seeks to design foundational pricing, procurement, and scheduling policies that ensure that users are distributed well across the platform and apply these policies to the emerging application of edge computing.Optimal dynamic pricing schemes can signal to users which providers have resources available, while conversely dynamic procurement allows networked providers to adjust their resources to user demands. Scheduling schemes complement pricing and procurement solutions by leveraging time flexibility in user demands to best allocate resources to users. While several works have separately considered optimal pricing and scheduling policies for networked platforms, this project is the first to develop foundational theories for the joint formulation of dynamic pricing/procurement and scheduling under uncertainty. This project will develop pricing, procurement, and scheduling algorithms with theoretical performance guarantees; combine these solutions with learning-based approaches to manage tradeoffs between robustness and performance; and validate these solutions in edge computing scenarios.Successful development of the proposed pricing, procurement, and scheduling solutions will make the business of edge computing more profitable and competitive. Providers may gain insights into how to best price their resources, while users may gain flexibility that helps lower the cost of fulfilling their demands. Further, the theoretical tools developed will make foundational contributions to online optimization and learning research. In addition to these technical broader impacts, the project will support several education and outreach activities. These will include undergraduate research projects, integration of the research findings into courses at the participating institutions, and presentations and interactive sessions at workshops aimed at broadening participation in computing.The results of this project will be maintained in an online repository to be hosted by either UMass or CMU. These are expected to include technical reports of the research findings, software prototypes of the algorithms designed, and datasets and experimental results collected for the edge computing experiments. The material in the repository will remain available for at least two years after the project concludes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.