Department of Electrical, Computer and Biomedical Engineering
University of Rhode Island
报告摘要：With the rapid growth of wireless compute-intensive services (such as image recognition, real-time language translation, or other artiﬁcial intelligence applications), efﬁcient wireless algorithm design should not only address when and which users should transmit at each time instance (referred to as wireless scheduling) but also determine where the computation should be executed (referred to as ofﬂoading decision) with the goal of minimizing both computing latency and energy consumption. Despite the presence of a variety of earlier works on the efﬁcient ofﬂoading design in wireless networks, to the best of our knowledge, there does not exist a work on the realistic user-level dynamic model, where each incoming user demands for a heavy computation and leaves the system once its computing request is completed. In this talk, we will consider the optimal ofﬂoading design in the presence of dynamic compute-intensive applications in wireless networks. In particular, we will show that there exists a fundamental logarithmic energy-workload tradeoff for any feasible ofﬂoading algorithm, and develop an optimal threshold-based ofﬂoading algorithm that achieves this fundamental logarithmic bound.
报告人简介：Bin Li received his B.S. degree in Electronic and Information Engineering in 2005, M.S. degree in Communication and Information Engineering in 2008, both from Xiamen University, and Ph.D. degree in Electrical and Computer Engineering from The Ohio State University in May 2014. Between June 2014 and August 2016, he was a Postdoctoral Researcher working with Prof. R. Srikant in the Coordinated Science Lab at the University of Illinois at Urbana-Champaign. In August 2016, he joined the University of Rhode Island as an assistant professor in the Department of Electrical, Computer and Biomedical Engineering. His research spans wireless networks, virtual and augmented reality, fog computing, and data centers. In particular, his research utilizes mathematical tools from stochastic processes, optimization, control, and algorithms to understand fundamental performance limits of complex network systems, and develop efficient, adaptable, and scalable algorithms for diverse applications.
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