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Intelligent task prediction and computation offloading based on mobile-edge cloud computing
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DOI：10.1016/j.future.2019.09.035.png)
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Abstract
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Edge computing overcomes the high communication delay shortcoming of traditional cloud computing and provides computing services with high reliability and high bandwidth for mobile devices. At present, edge computing has become the forefront and hotspot of mobile-edge cloud computing (MEC) research. However, with the increasing requirements and services of mobile users, offloading strategy of simple edge computing is no longer applicable to MEC architecture. This paper puts forward a new intelligent computation offloading based MEC architecture in combination with artificial intelligence (AI) technology. According to the data size of computation task from mobile users and the performance features of edge computing nodes, a computation offloading and task migration algorithm based on task prediction is proposed. The computation task prediction based on LSTM algorithm, computation offloading strategy for mobile device based on task prediction, and task migration for edge cloud scheduling scheme are used to assist in optimizing the edge computing offloading model. Experiments show that our proposed architecture and algorithm can effectively reduce the total task delay with the increasing data and subtasks. (C) 2019 Published by Elsevier B.V.
Keywords:
Artificial intelligence
Computation offloading
Edge computing
LSTM
Task migration
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