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 |
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01.12.2019
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Cao, Zhiying Zhang, Yingjun Zhang, Xiuguo Wu, Jinze |
| Author_xml | – sequence: 1 givenname: Jinze surname: Wu fullname: Wu, Jinze organization: Dalian Maritime University, China – sequence: 2 givenname: Zhiying surname: Cao fullname: Cao, Zhiying organization: Dalian Maritime University, China – sequence: 3 givenname: Yingjun surname: Zhang fullname: Zhang, Yingjun organization: Dalian Maritime University, China – sequence: 4 givenname: Xiuguo surname: Zhang fullname: Zhang, Xiuguo organization: Dalian Maritime University, China |
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| Snippet | 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,... |
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| SubjectTerms | Collaboration computation offloading Computational modeling Convergence Costs delay Delays edge-cloud collaborative Energy consumption Heuristic algorithms MEC Mobile handsets Particle swarm optimization Servers |
| Title | Edge-Cloud Collaborative Computation Offloading Model Based on Improved Partical Swarm Optimization in MEC |
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