Edge-Cloud Collaborative Computation Offloading Model Based on Improved Partical Swarm Optimization in MEC

In order to reduce the delay and energy consumption of mobile devices, a computational offload strategy is adopted in mobile edge computing (MEC). At present, most computation offloading strategies only consider two computing resources, mobile devices and MEC servers. However, the computing power of...

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Vydáno v:2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) s. 959 - 962
Hlavní autoři: Wu, Jinze, Cao, Zhiying, Zhang, Yingjun, Zhang, Xiuguo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2019
Témata:
Collaboration
computation offloading
Computational modeling
Convergence
Costs
delay
Delays
edge-cloud collaborative
Energy consumption
Heuristic algorithms
MEC
Mobile handsets
Particle swarm optimization
Servers
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Shrnutí:In order to reduce the delay and energy consumption of mobile devices, a computational offload strategy is adopted in mobile edge computing (MEC). At present, most computation offloading strategies only consider two computing resources, mobile devices and MEC servers. However, the computing power of the cloud server is much larger than that of the MEC server. Tasks with high computational complexity still need to be handed over to the cloud server for processing. This paper proposes an edge-cloud collaborative multi-task computing unloading model that considers both latency and energy cost. Usually the model solving is transformed into a search solution in finite strategy space. In this paper, the nonlinear exponential inertia weight particle swarm optimization (PSO) algorithm is used to get solution. By dynamically adjusting the inertia weight, the algorithm can make up for the convergence premature defect of the standard particle swarm optimization algorithm, and effectively avoid falling into the local optimal solution. Simulation experiments show that the strategy obtained by the model has lower total cost compared with different computation offloading models and strategies.
DOI:10.1109/ICPADS47876.2019.00144