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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Luo, Luoyang Guangdong University of Science and Technology College of Computer Science Dongguan523083 China

ISBN: (纸本)9798350341546

In Internet of Things (IoT) application scenarios, the traditional method of prioritizing task offloading based on scalar information such as maximum tolerable latency fails to meet the processing requirements of urgent tasks. To ensure the prioritized handling of urgent tasks, this paper proposes a priority task offloading decision optimization strategy based on the multi-objective Grey Wolf Algorithm. The research focuses on the problem of priority task offloading decision and considers the edge server task processing program cache. The optimization objectives, including minimizing overall latency, social loss rate, and load imbalance, are established to formulate a multi-objective optimization task offloading decision model. The multi-objective Grey Wolf Algorithm is employed to solve the problem. Experimental results demonstrate that the proposed multi-objective Grey Wolf Algorithm effectively reduces overall latency and social loss rate, optimizes load balancing among edge servers, and outperforms other algorithms significantly. © 2023 IEEE.

关键词： Edge Computing Multi-Objective Grey Wolf Algorithm offloading decision optimization Priority Task

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Joint Resource Allocation and offloading decision in Mobile Edge Computing

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IEEE COMMUNICATIONS LETTERS 2019年第4期23卷 684-687页

作者： Khalili, Ata Zarandi, Sheyda Rasti, Mehdi Amirkabir Univ Technol Dept Comp Engn & IT Tehran 158754413 Iran

In this letter, we investigate joint radio resource allocation and edge offloading decision optimization in a multi-cell orthogonal frequency-division multiple access cellular network in order to minimize system's energy consumption. The discrete subchannel allocation and offloading decision variables as well as the interference incorporated in the data rate function make the stated optimization problem a mixed integer non-linear programming. To address this problem and obtain a locally optimal solution, we use the variable relaxation and majorization minimization method. Via simulation results, we demonstrate that our proposed solution results in a considerable reduction of system's power consumption.

关键词： offloading decision optimization edge computing majorization minimization method

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Task offloading and Resource Allocation for Edge-Cloud Collaborative Computing 23rd

Task Offloading and Resource Allocation for Edge-Cloud Colla...

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23rd International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Wang, Yaxing Hao, Jia Xu, Gang Huang, Baoqi Zhang, Feng Inner Mongolia AR Key Lab Wireless Networking & M Hohhot Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan Peoples R China

ISBN: (纸本)9789819708079;9789819708086

Current research mainly focuses on two-tier cloud networks with edge clouds and users. The computational power relocation and resource allocation problem is relatively under-researched in three-tier networks consisting of multiple mobile users (MUs), multiple edge servers, and a centralized cloud. This paper constructs a system energy minimization problem based on a three-tier network by optimizing the computational resource allocation and offloading decision. Given the complexity of the problem, the optimization problem is decomposed into a resource allocation problem and a offloading decision problem, the Kuhn-Munkres offloading Algorithm (KOA) is proposed to minimize the total system energy consumption by optimizing the offloading decisions. The simulation results suggest that the method performs well.

关键词： Mobile edge computing offloading decision optimization Edge-Cloud collaboration

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