Task scheduling method based on deep reinforcement learning in hierarchical edge computing environment
A task scheduling and edge computing technology, applied in the computer field, can solve problems affecting mobile application experience, long data transmission delay, etc., and achieve the effects of low overhead, reduced service delay, and improved quality
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[0060] Embodiments of the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and therefore are only examples, rather than limiting the protection scope of the present invention.
[0061] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application shall have the usual meanings understood by those skilled in the art to which the present invention belongs.
[0062] like figure 1 As shown, the scenario of the present invention is applicable to the edge network scenario. The mobile application offloads its own resource-intensive tasks to the edge cloud through the nearby connected base station (Base station, BS). Understand the current available IT resources for each edge service node. After the task is offloaded to the edge cloud, CC r...
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