D2D communication collaboration based task unloading algorithm

Abstract

The invention discloses a D2D communication collaboration based task unloading algorithm. The algorithm includes steps of S1, initiating all tasks to execute locally, and calculating energy consumption and execution time delay Ti(0) of the tasks based on a local execution mode; S2, in an MEC environment, calculating the energy and the execution time delay of a single task in a specific execution mode; S3, in the MEC environment, comparing the energy consumption and the execution time delay of the single task based on the specific execution mode with the energy consumption and the execution time delay of the single task based on the local execution mode and calculating the corresponding energy consumption-execution time delay ratio according to a comparison result; S4, selecting the optimalexecution mode of the single task in the MEC environment according t the energy consumption-execution time delay ratio; S5, executing S2-S4 in circulation until the optimal execution modes of all thetasks in the MEC environment are obtained. According to the invention, through determining the execution mode of each task and through adding cooperative communication and cooperative calculation, multiple selectable execution modes are provided for a device, so that transmission time delay is reduced effectively and device energy consumption is reduced.

Description

technical field [0001] The present invention relates to the technical field of mobile edge computing, in particular to a task offloading algorithm on D2D. Background technique [0002] With the rapid development of the Internet and the wide application of wireless communication devices (smart phones, wearable devices, and Internet of Vehicles, etc.), users have higher and higher requirements for network data transmission speed and network delay. With the increase of mobile device applications, Mobile devices need to perform intensive and large-scale calculations, and the computing power of the devices themselves is limited. Therefore, large-scale tasks on the devices need to be migrated. The emergence of mobile edge computing can effectively expand the computing power of mobile devices. Wireless devices can partly or completely Intensive computing tasks and latency-sensitive tasks are offloaded to edge servers for execution. [0003] The offloading of mobile device tasks an...

AI Technical Summary

Problems solved by technology

In mobile edge computing, cooperative communication and collaborative computing between devices can effectively reduce the energy consumption and delay of mobile devices, and thus become a research hotspot in mobile edge computing. In communication, data transmission mostly adopts the decoding and forwarding method for data transmission, but in D2D mode, as the distance between devices increases, the decoding and forwarding method is used for data transmission, and the energy consumption and delay will increase rapidly

Secondly, there are the following disadvantages: frequent adjustment of device voltage and CPU frequency, in D2D mode, will reduce the service life of the device and increase additional energy consumption; failure to consider cooperative computing and cooperative transmission between devices, when the device offloads tasks to the edge server , the data transmission rate is low, which increases the transmission delay and energy consumption of the device

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