Fair allocation method for mobile edge computing shared resources under task deployment constraints

Abstract

The invention discloses a fair allocation method for mobile edge computing shared resources under task deployment constraints. The method comprises the following steps of firstly, determining an edgecomputing server set and available resource capacity; obtaining a resource demand vector and a task deployment constraint vector of a user; calculating the maximum number of tasks which can be executed under the condition that the user monopolizes resources and setting the weight of the user; when resources allocation, setting active user sets and allocating the active users based on a task sharemaximum and minimum allocation principle; with the task shares of other inactive users unchanged, screening the active users after each round of allocation is completed, wherein the users whose taskshares cannot be continuously increased in the next round of resource allocation are converted into the inactive users, and finishing resource allocation until all the users are the inactive users. The individual interests of the users are fully guaranteed under the task deployment constraint condition of the edge computing environment, and the resource allocation efficiency and fairness are improved.

Description

technical field [0001] The invention belongs to the technical field of cloud computing, and more specifically, relates to a method for fairly allocating shared resources of mobile edge computing under task deployment constraints. Background technique [0002] Mobile edge computing, as a new computing model, is more convenient to serve a large number of services that are sensitive to network delay and computing delay at the edge side than traditional cloud computing due to its computing sinking characteristics. With the development of the Internet of Things and 5G networks, mobile edge computing is changing our lives little by little. According to reports, in 2018, the scale of my country's Internet of Things industry has exceeded 1.2 trillion yuan, and the Internet of Things business has increased by 72.9% compared with the previous year. Such a large application scale and rapid growth trend have made mobile edge computing gain popularity from academia and industry. More and...

AI Technical Summary

Problems solved by technology

However, the current allocation strategy for edge computing resources ignores the contradiction between the heterogeneity of these server configurations and the deployment restrictions of user tasks. For example, image recognition tasks can only be deployed on servers with GPUs, while domain name resolution (DNS ) applications need to be deployed on a server with a public IP, etc.

Due to the existence of these task deployment constraints, it will greatly increase the delay of user task deployment and reduce resource utilization.

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