LI

NS

research
group

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.

    Y. Jiang, M. Shahrad, D. Wentzlaff, D. H. K. Tsang and C. Joe-Wong, Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization, accepted to IEEE INFOCOM 2019.
    M. Harishankar, P. Tague and C. Joe-Wong, Network Slicing as an Ad-Hoc Service: Opportunities and Challenges in Enabling User-Driven Resource Management in 5G, Workshop on Trustworthy & Real-time Edge Computing for Cyber-Physical Systems (TREC4CPS), co-located with IEEE RTSS 2018.
    M. Harishankar, N. Srinivasan, C. Joe-Wong and P. Tague, To Accept or Not to Accept: The Question of Supplemental Discount Offers in Mobile Data Plans, IEEE INFOCOM 2018.
    M. Khodak, L. Zheng, A. S. Lan, C. Joe-Wong and M. Chiang, Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies, IEEE INFOCOM 2018.
    L. Zheng, C. Joe-Wong, M. Andrews and M. Chiang, Optimizing Data Plans: Usage Dynamics in Mobile Data Networks, IEEE INFOCOM 2018.

Networked Learning

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.

    Y. Ruan, L. Zheng, M. Gorlatova, M. Chiang and C. Joe-Wong, The Economics of Fog Computing: Pricing Tradeoffs for Data Analytics, to appear in Fog and Fogonomics: Challenges and Practices of Fog Computing, Networking, Strategy and Economics, Wiley, 2019.
    J. Zuo, X. Zhang and C. Joe-Wong, Observe before Play: Multi-armed Bandit with Pre-Observations, ACM SIGMETRICS Work-in-Progress 2018.

Networked Communities

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).

    P. Kortoci, L. Zheng, C. Joe-Wong, M. Di Francesco and M. Chiang, Fog-based Data Offloading in Urban IoT Scenarios, accepted to IEEE INFOCOM 2019.
    W. Chen, C. Joe-Wong, C. G. Brinton, L. Zheng and D. Cao, Principles of Assessing Adaptive Online Courses, Educational Data Mining 2018.
    T. Oda and C. Joe-Wong, MOVI: A Model-Free Approach to Dynamic Fleet Management, IEEE INFOCOM 2018.
    T.-Y. Yang, C. G. Brinton and C. Joe-Wong, Predicting Learner Interactions in Social Learning Networks, IEEE INFOCOM 2018.
    A. Jauhri, C. Joe-Wong and J. P. Shen, On the Real-Time Vehicle Placement Problem, NIPS Workshop on Machine Learning for Intelligent Transportation Systems 2017.