HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing
Volume: 11 - Issue: 01 - Date: 01-01-2022
Approved ISSN: 2278-1412
Published Id: IJAECESTU363 | Page No.: 141-151
Author: Reeta Kushwaha
Co- Author: Chhatrapani Gautam
Abstract:-Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive
radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation
offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We
show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for
achieving efficient computation offloading in a distributed manner. We formulate the distributed computation
offloading decision making problem among Mobil e device users as a multi-user computation offloading game. We
analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the
finite improvement property. Then design a distributed computation offloading algorithm that can achieve a Nash
equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized
optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of
multi-user computation offloading in the multi-channel wireless contention environment. Numerical results
corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as
the use r size increases
Key Words:-Edge AI, Deep Learning, Fast Model Training, Mobile-Edge-Cloud Computing
Area:-Engineering
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