Dynamic task allocation method for energy-delay balance in rechargeable mobile edge calculation

Abstract

Energy-in-charge mobile edge computing The delay-balanced dynamic task allocation method comprises the following steps: S1, setting that task execution generated by a request sent by mobile equipment comprises three modes; S2, establishing a mathematical model of task optimization distribution generated by the request sent by the mobile device; S3, collecting and initializing corresponding parameters; S4, collecting energy Eh (1) and channel power gain H (1) captured by the mobile equipment in the first time slot, and calculating virtual power supply energy queues S5 to S11: dividing the objective function into an energy optimization part and a task allocation optimization part to respectively solve optimal values, and updating corresponding parameters; S12, repeating the steps S6 to S11 until a dynamic iteration stop condition is met. Three modes for executing the task of the task mobile device are designed, the task is executed in a low-power-consumption mode under the condition that an original power source is insufficient, the task abandoning rate is reduced, environmental protection is achieved, energy consumption and delay cost are further considered, and the task distribution and calculation efficiency of the mobile device is improved.

Description

Technical field [0001] The present invention relates to the technical field of task allocation calculation of mobile devices, and in particular to a dynamic task allocation method of energy-delay balance in rechargeable mobile edge calculation. Background technique [0002] The increasing popularity of mobile devices such as smartphones, tablets, and wearable devices has accelerated the development of Internet of Things technology and triggered a revolution in mobile applications. More and more large-scale and complex computing applications have emerged. However, as the industry of smart mobile devices tends to be miniaturized and thinner, there are still many technical bottlenecks when facing large-scale computing tasks. First, its computing power still cannot meet the requirements of complex mobile applications, and it takes a long time to obtain the calculation results; second, its own battery capacity is not enough to support long-term complex calculations, which affects the ...

AI Technical Summary

Problems solved by technology

First of all, the energy harvested by energy harvesting devices from the environment is variable, and the energy consumption of mobile devices at every moment is also unpredictable, so how to ensure the stability of battery power during application execution is a problem we need to pay attention to

Secondly, the more complex the system is, the more factors will affect the unloading decision. How to design an algorithm to ensure the efficient operation of the system is also a challenge.

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