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

今天，许多行业都有某种网络平台，消费者可以从供应商网络购买资源。例如，移动的边缘用户可以形成一个网络，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，他们应该确保他们的分布式资源定价合理，并以适当的数量提供给用户。否则，一些供应商可能会发现自己用户过多，无法满足他们的需求。卡内基梅隆大学（CMU）和马萨诸塞州阿默斯特大学（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。最佳动态定价方案可以向用户发出信号，告知哪些提供商有可用资源，而相反，动态采购允许联网的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向用户分配资源，从而补充定价和采购解决方案。虽然有几个作品分别考虑了网络平台的最优定价和调度政策，该项目是第一个开发的基础理论，联合制定动态定价/采购和调度下的不确定性。该项目将开发具有理论性能保证的定价、采购和调度算法;联合收割机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。成功开发拟议的定价、采购和调度解决方案将使边缘计算业务更具盈利能力和竞争力。供应商可以深入了解如何最好地定价其资源，而用户可以获得灵活性，这有助于降低满足其需求的成本。此外，开发的理论工具将为在线优化和学习研究做出基础性贡献。除了这些广泛的技术影响外，该项目还将支持若干教育和外联活动。这将包括本科生研究项目，研究成果融入课程的参与机构，并在研讨会上的演示和互动会议，旨在扩大参与计算。该项目的结果将被保存在一个在线存储库由麻省大学或CMU主办。预计这些报告将包括研究结果的技术报告、所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。该奖项反映了NSF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.