Semi-Markov decision process-based task unloading method for vehicle-mounted fog computing system

Abstract

The invention provides a semi-Markov decision process-based task unloading method for a vehicle-mounted fog computing system. According to the semi-Markov decision process-based task unloading methodprovided by the invention, various time delays can be comprehensively considered according to the actual situation of the task unloading process. An unloading strategy which better conforms to the actual situation is obtained, so that the system obtains more long-term benefits. The method comprises the following steps: S1, defining a state set of a system based on a semi-Markov decision model; S2,defining an action set of the system; S3, defining a reward model of the system; S4, defining the transition probability of the system; S5, solving an optimal unloading strategy in the vehicle-mounted fog computing system; the method is characterized in that in the step S3, a system reward can be expressed as a difference value of immediate income and expenditure; calculation of the immediate income is carried out through different time delays, including the time delay needed by a local processing task. The transmission time delay sent to the computing unit by the vehicle is requested. The system unloads the task to the computing unit to process the needed time delay.

Description

technical field [0001] The invention relates to the technical field of wireless communication for vehicles, in particular to a task offloading method for a vehicle fog computing system based on a semi-Markov decision process. Background technique [0002] The development of emerging in-vehicle applications such as autonomous driving, video streaming, and in-vehicle games has created a large number of computationally complex and delay-sensitive tasks in the vehicle control process. In-vehicle fog computing is an effective solution to take advantage of the abundant computing resources and reliable wireless connectivity of modern vehicles. The core of vehicular fog computing is to recruit surrounding vehicles with idle resources as fog servers. Offloading tasks from cloud / edge servers to nearby and densely distributed fog servers can ensure fast processing of computing tasks in the local network and meet the real-time requirements of unmanned driving and other tasks. [0003]...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem in the prior art that the types of time delays are not comprehensively considered, resulting in unloading strategies that are not practical enough, the present invention provides a semi-Markov decision-making process based The task offloading method of the vehicle fog computing system can comprehensively consider various time delays according to the actual situation of the task offloading process, and obtain an offloading strategy that is more in line with the actual situation, so that the system can obtain more long-term benefits

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