Collaborative computing unloading and resource allocation method and application

Abstract

The invention belongs to the technical field of wireless communication. The invention discloses a collaborative computing unloading and resource allocation method and application. According to the method, the weighted sum of computing rate of mobile edge computing and the throughput of a block chain system is maximized by jointly optimizing collaborative unloading decisions, power allocation and the block size and the block time of the block chain system; optimal compromise of the performance of two subsystems is realized. Specifically, firstly, a computing task of a mobile device is unloadedto an MEC server to be executed through cooperative communication; secondly, a computing unloading transaction from an MEC system is processed by adopting a block chain technology; in order to ensurethe security of data in an integrated system, a trust computing model is considered in cooperative unloading and block generation. According to the method, a joint optimization problem is converted into a Markov decision process problem, and an efficient unloading decision and resource allocation algorithm based on deep reinforcement learning is put forward.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a collaborative computing offloading and resource allocation method and application. Background technique [0002] At present, the closest existing technology: mobile edge computing (MEC) improves the computing power of mobile devices by offloading computing tasks on mobile devices to MEC service execution, so it is a very promising technology, which is applied In various fields such as Vehicle Networks, Internet of Things and Smart City. Compared with the centralized cloud computing system (Cloud Computing System), the distributed structure of the MEC system has many advantages, including reducing energy consumption and reducing delay. A large number of literatures have done in-depth research on the computing offloading and resource allocation of MEC, however, due to the following challenges, these existing methods are not suitable for application in ...

AI Technical Summary

Problems solved by technology

[0004] 2. Collaborative computing offloading, an approach that has only been considered by a few researchers in previous work

[0006] (1) The existing design and optimization of MEC and blockchain integrated systems are done independently, and the unbalanced allocation of resources will lead to suboptimal system performance

[0007] (2) Existing mobile devices that are far away from the MEC server cannot directly offload computing tasks to the MEC server. If there are malicious nodes near the mobile device, the security and privacy of its data are vulnerable

However, if there are malicious behaviors in the surrounding nodes, such as discarding packets or leaking user information, how to ensure the security and privacy of data in collaborative offloading is an important challenge

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