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

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.