Learning, Incentives, and Optimization
for Networked Systems
Our research falls into three overlapping areas: smart data pricing, networked learning, and networked communities. We also maintain pages on specific projects, e.g., see here for an overivew of work done under Carlee's NSF CAREER award.
Smart Data Pricing
Initiatives like edge and fog computing are moving applications to an ever-greater array of devices in the Internet-of-Things, ranging from cloud servers to smartphones to low-power sensors. Yet relatively little work has examined whether this explosion is economically sustainable. Our work aims to do so by developing economic theories of markets for data and computing resources that underlie the success of the Internet, and analyzing the effectiveness of existing pricing mechanisms. Some of our most recent publications consider ways in which users can pay for dynamic amounts of resources that match their demands at different times, as offered today by burstable instances for cloud computing and supplemental discounts for mobile data plans.
Many emerging applications that utilize paradigms like edge and fog computing are based on machine learning and artificial intelligence, which have achieved remarkable success in a range of areas like vision, gaming, and autonomous vehicles. However, running conventional ML algorithms on networks of (possibly unreliable) devices may induce complex cost and performance tradeoffs that are not yet well understood. Conversely, using machine learning to define operating policies in networked systems often requires developing new algorithms that explicitly account for user competition and cooperation. Our work develops and analyzes new algorithms for such networked settings. Some of our ongoing work specifically focuses on distributed multi-armed bandit frameworks for sharing unlicensed spectrum bands and deriving runtime, accuracy, and cost tradeoffs for running distributed regression algorithms.
The pervasiveness of Internet-connected devices has enabled new types of physical and virtual communities in which users can proactively cooperate with each other to accomplish shared or individual goals. Our work examines how users in cities and online platforms can be incentivized to productively interact with each other. Some of our most recent work considers cooperation between vehicles in urban settings and user interaction in the virtual communities created by MOOCs (massive open online courses).