Mobile edge computing(MEC) is a cloud server running at the edge of a mobile network, which can effectively reduce network communication delay. However, due to the numerous edge servers and devices in the MEC, there m...
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Mobile edge computing(MEC) is a cloud server running at the edge of a mobile network, which can effectively reduce network communication delay. However, due to the numerous edge servers and devices in the MEC, there may be multiple servers and devices that can provide services to the same user simultaneously. This paper proposes a userside adaptive user service deployment algorithm ASD(Adaptive Service Deployment) based on reinforcement learning algorithms. Without relying on complex system information, it can master only a few tasks and users. In the case of attributes, perform effective service deployment decisions, analyze and redefine the key parameters of existing algorithms, and dynamically adjust strategies according to task types and available node types to optimize user experience delay. Experiments show that the ASD algorithm can implement user-side decision-making for service deployment. While effectively improving parameter settings in the traditional Multi-Armed Bandit algorithm,it can reduce user-perceived delay and enhance service quality compared with other strategies.
With the development and construction of new technologies, the existing centralized data processing mode of ICS is limited by the lack of storage resources and computing power, and cannot meet the needs of massive dat...
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ISBN:
(纸本)9798400716300
With the development and construction of new technologies, the existing centralized data processing mode of ICS is limited by the lack of storage resources and computing power, and cannot meet the needs of massive data processing and frequent interaction between devices, which makes the ICS cloud-edge-end collaboration technology receive more and more attention. Based on this, this paper combs the concept of ICS cloud-edge-end collaboration, introduces the network architecture of ICS cloud-edge-end collaboration, and analyzes the interaction mode and key support technologies of ICS cloud-edge-end collaboration. By summarizing the current research situation of ICS cloud-edge-end collaboration technology, the future development is prospected.
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