Guest lecture - MengChu Zhou

MengChu Zhou, Professor at the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology gives the lecture Energy-Optimized Partial Computation Offloading for Delay-Sensitive Applications in Heterogeneous Mobile Edge Computing

The lecture is organized by the Electrical Power Systems Laboratory (EPSLab) at the Engineering Institute, University of Iceland and will also be held via Teams.

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Abstact

Mobile edge computing (MEC) is an emerging architecture that supports computing, storage, and    networking resources to users’ mobile devices (MDs). MDs have limited resources and energy capacity, and they have to offload some tasks of computational/delay-intensive applications to their nearby small base station (SBS), which is a paradigm of MEC. Although task offloading decreases energy consumed by MDs, it brings additional transmission delay among MDs and SBS/cloud data center (CDC), and processing delay in SBS and CDC. This talk considers two challenging research problems in MEC. First, this talk introduces a constrained optimization problem to minimize the total cost of a heterogeneous system including MDs, an SBS, and a CDC while strictly guaranteeing delay limits of tasks. Second, this talk further considers a more complicated scenario including both macro base stations (MBSs) and small base stations (SBSs) where resource-limited MDs are associated with them. This talk then introduces a partial computation offloading approach for delay-sensitive applications in such a hybrid network including MDs, SBSs, and an MBS. The total energy consumption minimization is formulated as a constrained mixed-integer nonlinear program. It jointly optimizes task offloading among MDs, SBSs, and MBS, users’ connection to SBSs, MDs CPU speeds and transmission power, and channels bandwidth allocation. To solve the above two problems, a hybrid method named Genetic Simulated-annealing-based Particle swarm optimization (GSP) is proposed and it integrates genetic operations of genetic algorithm and the Metropolis acceptance rule of simulated annealing into particle swarm optimization. Simulations with real-world data demonstrate that GSP significantly outperforms other methods in the energy consumption of the system.

About the speaker

MengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, Beijing, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY in 1990.

He joined New Jersey Institute of Technology (NJIT), Newark, NJ in 1990, and is now Distinguished Professor in Electrical and Computer Engineering.
His research interests are in Petri nets, intelligent automation, Cloud/edge Computing, Internet of Things, big data, web services, and intelligent transportation.

He has over 1000 publications including 13 books, 700+ journal papers (600+ in IEEE transactions), 30 patents and 29 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering, Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica, and Associate Editor of IEEE Internet of Things Journal, IEEE Transactions on Intelligent Transportation Systems, and IEEE Transactions on Systems, Man, and Cybernetics: Systems.

He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society, Excellence in Research Prize and Medal from NJIT, and Edison Patent Award from the Research & Development Council of New Jersey.

He is highly cited scholar with over 50,300 Google Scholar citations and h-index 113. He is a Fellow of IEEE, International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS), Chinese Association of Automation (CAA) and National Academy of Inventors (NAI).