A User-Centric Distributed Multi-User Computing Task Offloading Method

Abstract

The invention provides a user-centered distributed multi-user computing task offloading method, which is characterized in that it includes the following steps: a plurality of fog computing computing nodes are uniformly abstracted into an access point with a server, and the access point has both Computing and communication capabilities, when any user generates a computing task, comprehensively consider the unloading decisions of other users, and choose one of the local computing or offloading computing tasks to other computing nodes according to the unloading decision under the current time slot to minimize the self overhead. The invention proposes a user-centered distributed multi-user calculation task offloading method. Such methods can realize stable and efficient offloading of computing tasks among multiple users in a distributed execution manner. The present invention can not only achieve satisfactory results for users, but also ensure better overall performance of the system (compared with the optimal strategy of the system).

Description

technical field [0001] The invention relates to a user-centered distributed computing task unloading method among multiple users, which can realize stable and efficient computing task unloading among multiple users in a distributed execution manner, achieve satisfactory results for users, and ensure better overall system performance. Background technique [0002] With the rapid development of the Internet of Things, 5G communications, and artificial intelligence, a large number of new applications and services are also emerging, such as online games, ultra-high-definition video, and augmented reality, while terminal devices are showing explosive growth. On the one hand, a large amount of various data generated by these applications and devices requires a lot of resources to process; on the other hand, terminal devices generally have limited resources due to limitations in physical size and energy consumption. In order to alleviate this contradiction, mobile cloud computing ...

AI Technical Summary

Problems solved by technology

Although there have been a lot of related research work, with various metrics as optimization goals (delay, energy consumption or comprehensive consideration of delay and energy consumption), most of the research work has the following more or less deficiencies: 1) The current research work focuses on the simple scenario of multiple users, single computing node or single user, multiple computing nodes. For the more common but complex scenario of multiple users, multiple computing nodes, there is still a lack of systematic research ; 2) The current research work mostly starts from the perspective of the system, ignoring the differences in performance and goals pursued by heterogeneous users

This kind of method will cause damage to the user's own interests, thus not following the distribution results of the system, and eventually making the whole method invalid; 3) The current research work is mostly a centralized method, which is not suitable for large-scale and distributed application scenarios in the future

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