Seminar on Energy-efficient Task Offloading Using Hybrid Particle Swarm Optimization with Genetic Operations in Smart Edge

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IEEE North Jersey Section-SMC Chapter Seminar

Energy-efficient Task Offloading Using Hybrid Particle Swarm Optimization with Genetic Operations in Smart Edge

Speaker: Haitao Yuan, Associate Professor

School of Automation Science and Electrical Engineering

Beihang University, Beijing, 100191, China

yuanhaitao@buaa.edu.cn

Place: https://njit.webex.com/join/zhou

Time: 9pm, Thur. night, Nov. 5, 2020

 

Abstract: Smart mobile devices (SMDs) can meet users’ high expectations by executing computational intensive applications but they only have limited resources including CPU, memory, battery power and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth, and inevitably increases latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve the balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels and transmission power of each SMD in each time slot. It jointly considers execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid meta-heuristic algorithm named Genetic Simulated-annealing-based Particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life databased experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.

 

Biography: Haitao Yuan received the Ph.D. degree in Computer Engineering from New Jersey Institute of Technology (NJIT), Newark, NJ, USA in 2020. Before that, he received his Ph.D. degree in Modeling Simulation Theory and Technology from Beihang University, Beijing, China in 2016 and the M.S. and B.S. degrees in Software Engineering from Northeastern University, Shenyang, China, in 2010 and 2012, respectively. He is currently an Associate Professor with the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. He was an Associate Professor with the School of Software Engineering, Beijing Jiaotong University, Beijing, China. He has over 70 publications in international journals and conference proceedings, including IEEE Internet of Things Journal, ACM Transactions on Internet Technology, IEEE Transactions on Cloud Computing, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Services Computing, IEEE Transactions on Industrial Informatics and IEEE Transactions on Cybernetics. His research interests include cloud computing, edge computing, data centers, big data, machine learning, deep learning and optimization algorithms. Dr. Yuan was the recipient of the 2011 Google Excellence Scholarship and the recipient of the Best Paper Award in the 17th IEEE International Conference on Networking, Sensing and Control.

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8:50pm Social

9:00 Seminar

 

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