Abstract: With the recent proliferation of the Internet-of-Things (IoT), more and more devices now have computing capabilities and Internet connections. While these newfound capabilities have enabled a multitude of emerging applications, e.g., large-scale machine learning that takes as input data collected by many IoT sensors, they also raise new challenges for ensuring that such applications receive the data, computing, and communication resources that they need. Some data analytics tasks, for example, may require significantly more processing capabilities than others, and these capabilities may not always be available depending on the status of devices in the network. Optimizing such resource allocations across heterogeneous applications is in general NP-hard and becomes even more challenging in a dynamic and uncertain environment in which user demands and resource availability may change in unknown ways over time. In this talk, I will present our recent work using online and reinforcement learning techniques to provision both computing and communication resources for heterogeneous applications. By incorporating prior knowledge of the problem structure and application requirements, we can significantly accelerate our ability to learn how to allocate resources without requiring prior models of the environment. Our experiments on distributed machine learning and autonomous vehicle applications indicate that we can improve application performance and utilize fewer resources compared to static and naive learning-based baselines.
Bio: Carlee Joe-Wong is the Robert E. Doherty Associate Professor of Electrical and Computer Engineering at Carnegie Mellon University. She received her A.B. degree (magna cum laude) in Mathematics, and M.A. and Ph.D. degrees in Applied and Computational Mathematics, from Princeton University in 2011, 2013, and 2016, respectively. Her research interests lie in optimizing various types of networked systems, including applications of machine learning and pricing to cloud computing, mobile/wireless networks, and transportation networks. From 2013 to 2014, she was the Director of Advanced Research at DataMi, a startup she co-founded from her research on mobile data pricing. She received the NSF CAREER award in 2018 and the ARO Young Investigator award in 2019.