Learning and Prediction in Networked System Optimization

Machine learning and data mining algorithms have been receiving increasing attention in practice, with the ultimate objective of improving system performance and user experience. However, there has been limited theoretical understanding about how learning and prediction impact networked system optimization.

In this research thrust, we are interested in finding out fundamental benefits and limits of learning and prediction, and designing simple yet powerful algorithms for reaping the full benefit of them.

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Recent publications:

  1. L.Huang, M. Chen, and Y. Liu, ‘‘Learning-aided Stochastic Network Optimization with Imperfect State Prediction,’’ Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), July 2017.

  2. L. Huang, “System Intelligence: Model, Bounds and Algorithms,” Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), July 2016.

  3. L. Huang, ‘‘The Value-of-Information in Matching with Queues,’’ Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), June 2015.

  4. L. Huang, S. Zhang, M. Chen, and X. Liu ‘‘When Backpressure meets Predictive Scheduling,’’ Proceedings of 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), August 2014. (Best Paper Candidate)

  5. L. Huang, X. Liu, and X. Hao, ‘‘The Power of Online Learning in Stochastic Network Optimization,’’ Proceedings of ACM Sigmetrics (Sigmetrics full paper), June 2014.

Sharing Economy: Welfare and Revenue

Sharing economy has emerged as an enabling method for efficiently utilizing social resources that will otherwise have low-utilization. However, the growth of the sharing economy is driven mainly by sharing platforms, whose objectives might not be exactly aligned with social welfare. Thus, many interesting and important questions remain unclear.

In this research direction, we are interested in fundamental questions regarding social welfare, platform management, and incentive mechanisms.

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Recent publications:

  1. Z. Fang, L.Huang, and A. Wierman, ‘‘Prices and Subsidies in the Sharing Economy,’’ Proceedings of World Wide Web (WWW) (full paper), April 2017. [ArXiv Technical Report, arXiv:1604.01627]

Age-of-Information: Optimizing the Freshness of Information

Realtime status information is critical for cyber-physical systems, e.g., self-driving cars. In such systems, what matters most is not how fast the update information gets delivered, but rather, how accurately the received information describes the physical phenomenon being observed. Age-of-Information has thus emerged as a novel metric to quantify the ‘‘freshness’’ of information.

In this thrust, we are interested in quantifying and understanding the age-of-information in various systems, and designing algorithms to optimize age-of-information dependent performance.

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Recent publications:

  1. K. Chen and L. Huang, ‘‘Age-of-Information in the Presence of Error,’’ Proceedings of IEEE International Symposium on Information Theory (ISIT), July 2016.

  2. L. Huang and E. Modiano, “Optimizing Age-of-Information in a Multi-class Queueing System,” Proceedings of IEEE International Symposium on Information Theory (ISIT), June 2015.

Energy Management and Smart Grid

Operating our computing infrastructure at an energy-efficient mode is critical for making our planet a greener and better place. With the increasing penetration of renewal energy and storage technologies, which are dynamic and complicated in nature, optimizing energy management remains a challenging tasks.

In this research direction, we develop optimal energy management schemes for computing infrastructures and the power grid. Our research thrusts include (i) optimal energy management for computing infrastructures and (ii) energy storage and demand response in the smart grid.

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Recent publications:

  1. M. Hajiesmaili, C. Chau, M. Chen, and L. Huang, ‘‘Online Microgrid Energy Generation Scheduling Revisited: The Benefits of Randomization and Interval Prediction,’’ Proceedings of ACM e-Energy (e-Energy), June 2016. (Best Paper Candidate)

  2. L. Huang, “Optimal Sleep-Wake Scheduling for Energy Harvesting Smart Mobile Devices,” IEEE Transactions on Mobile Computing (TMC), vol. 16, issue 5, pp 1394-1407, May 2017.

  3. N. Edalat, J. Walrand, M. Mehul, and L. Huang, ‘‘A Methodology for Designing the Control of Energy Harvesting Sensor Nodes,’’ IEEE Journal of Selected Areas in Communications - Special Issue on Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer (JSAC), Vol. 33, issue:3, pp. 598-607, March 2015.

Reducing Latency in Stochastic Networks

Latency has been one of the most important performance metrics for many communication and computing systems. However, due to system dynamics and complex control options, optimizing system latency stays a challenging problem.

In this research, we aim to design delay-efficient algorithms and techniques for three general components in a general information system, including data storage systems, data networks, and multi-stage processing networks.

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Recent publications:

  1. S. Zhang, L. Huang, M. Chen, and X. Liu, ‘‘Proactive Serving Decreases User Delay Exponentially: The Light-tailed Service Time Case,’’ IEEE/ACM Transactions on Networking (TON), vol. 25, issue 2, 708-723, April 2017.

  2. L. Huang, ‘‘Receding Learning-aided Control in Stochastic Networks,’’ IFIP Performance (Performance), Oct 2015.

  3. L. Huang, S. Zhang, M. Chen, and X. Liu ‘‘When Backpressure meets Predictive Scheduling,’’ Proceedings of 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), August 2014. (Best Paper Candidate)