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

合作研究：CNS 核心：小型：分布式网络平台的动态定价和采购

• 批准号: 2102963
• 负责人: Mohammadhassan Hajiesmaili
• 金额: $ 20万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102963&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

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.

如今，许多行业都采用某种网络平台，消费者可以从提供商网络购买资源。例如，移动边缘用户可以形成网络并将其计算资源租给消费者。但是，为了使这些新兴的企业生存和成长，他们应确保其分布式资源的价格正确，并以适当的数量提供给用户。否则，一些提供者可能会发现自己对用户负载，无法满足他们的需求。 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网络提供商将其资源调整为用户需求。调度方案通过利用用户需求的时间灵活性来最好地将资源分配给用户，以补充定价和采购解决方案。尽管几项作品已分别考虑了网络平台的最佳定价和调度策略，但该项目是第一个开发基本理论，用于在不确定性下进行动态定价/采购和调度的联合制定。该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，以管理鲁棒性和绩效之间的权衡；并在边缘计算方案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Edge Computing的业务变得更加有利可图和竞争力。提供者可能会深入了解如何最佳的资源，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，开发的理论工具将为在线优化和学习研究做出基础贡献。除了这些技术更广泛的影响外，该项目还将支持几项教育和外展活动。这些将包括本科研究项目，将研究结果整合到参与机构的课程中，以及旨在扩大计算参与的研讨会的演讲和互动会议。该项目的结果将在在线存储库中维持，由UMass或CMU托管。这些预计将包括有关研究结果的技术报告，设计算法的软件原型以及用于边缘计算实验的数据集和实验结果。该项目结束后，存储库中的材料将至少可用两年。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks

电动汽车充电网络中联合定价和调度的近最优在线算法

• DOI: 10.1145/3575813.3576878
• 发表时间: 2023
• 期刊: Proceedings of the 14th ACM International Conference on Future Energy Systems
• 作者: Bostandoost, Roozbeh;Sun, Bo;Joe-Wong, Carlee;Hajiesmaili, Mohammad
• 通讯作者: Hajiesmaili, Mohammad

The War of the Efficiencies: Understanding the Tension between Carbon and Energy Optimization

效率之战：了解碳与能源优化之间的紧张关系

• DOI: 10.1145/3604930.3605709
• 发表时间: 2023
• 期刊: HotCarbon '23: Proceedings of the 2nd Workshop on Sustainable Computer Systems
• 作者: Hanafy, Walid A.;Bostandoost, Roozbeh;Bashir, Noman;Irwin, David;Hajiesmaili, Mohammad;Shenoy, Prashant
• 通讯作者: Shenoy, Prashant

On-Demand Communication for Asynchronous Multi-Agent Bandits

• DOI: 10.48550/arxiv.2302.07446
• 发表时间: 2023-02
• 作者: Y. Chen;L. Yang;Xuchuang Wang;Xutong Liu;M. Hajiesmaili;John C.S. Lui;D. Towsley

Distributed Bandits with Heterogeneous Agents

• DOI: 10.1109/infocom48880.2022.9796901
• 发表时间: 2022-01
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Lin Yang;Y. Chen;M. Hajiesmaili;John C.S. Lui;D. Towsley

Contextual Combinatorial Bandits with Probabilistically Triggered Arms

• DOI: 10.48550/arxiv.2303.17110
• 发表时间: 2023-03
• 作者: Xutong Liu;Jinhang Zuo;Siwei Wang;John C.S. Lui;M. Hajiesmaili;A. Wierman;Wei Chen