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Computing Offloading Strategy Using Improved Genetic Algorithm in Mobile Edge Computing System
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DOI：10.1007/s10723-021-09578-8.png)
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For the current research on computing offloading, most of them only considers the multi-user task offloading decision problem or only considers the wireless resource and computing resource allocation. They have failed to comprehensively consider the impact of offloading decision and resource allocation on computing offloading performance, and it is difficult to achieve efficient computing offloading. For this reason, this paper proposes an edge computing task offloading strategy based on improved genetic algorithm (IGA). First, the weighted sum of task execution delay and energy consumption is defined as the optimization function of total overhead. Besides, the paper comprehensively considers the impact of users' offloading decision, uplink power allocation related to task offloading and MEC computing resource allocation on system performance. Secondly, Genetic Algorithm (GA) is substituted to establish communication model, the offloading strategy is corresponding to the chromosome in algorithm and the gene is encoded by integer coding. Finally, IGA is used to solve the task to achieve efficient offloading. Among them, the use of integer coding, knowledge-based crossover and the mutation of population segmentation improves the optimization ability of this algorithm. Finally, experimental results show that the performance of IGA is the best, and the overall cost is about 52.7% of All-local algorithm and 28.8% of Full-edge algorithm.
Keyword:
Computing offloading
Mobile edge computing (MEC)
Improved genetic algorithm (IGA)
Computing resource
Task allocation
Offloading decision
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761
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Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks移动边缘计算网络中能量感知卸载的能量-延迟权衡
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分享收藏分享收藏Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems无线移动边缘计算系统中的联合卸载和计算优化
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