A Multi-access Edge Computing Task Offloading Method Based on Bin Packing Problem

Abstract

The present invention proposes a multi-access edge computing task offloading method based on the bin packing problem. The method regards user terminals and edge servers as task containers and tasks as items, thereby transforming the task offloading decision-making problem in edge computing into A bin packing problem, and then solve the task offloading decision by minimizing the number of enabled edge servers in the network through a heuristic method. It includes the following steps: first calculate the capacity of each edge server and the ratio of the input data size of each terminal task to the required computing resources; then form two queues according to the capacity and task ratio from large to small; finally The tasks in the task queue are sequentially taken out and allocated to the edge server with the largest capacity and remaining computing resources in the container queue, and this operation is repeated until the task queue is empty. The present invention is applicable to the task processing process of a multi-terminal multi-task multi-access edge computing network, can clearly formulate a suitable task offloading scheme, minimizes computing energy consumption while meeting task time delay requirements, and saves costs.

Description

technical field [0001] The invention relates to the field of multi-access edge computing, in particular to the problem of computing resource allocation of multiple edge servers in a multi-access edge computing network. Background technique [0002] The Internet of Things (IoT) technology aims to use radio frequency identification technology, wireless data communication technology, global positioning system, etc., to connect physical objects with the Internet according to the communication protocol agreed by the Internet of Things, and then realize information exchange and achieve intelligence. The purpose of identifying, locating, tracking, monitoring and managing Internet resources. With the increase in the number of devices, the enhancement of computing performance, and the continuous growth of user needs, edge computing has become an important expansion form of the Internet of Things. Multi-access Edge Computing (Multi-access Edge Computing, MEC) is an emerging concept i...

AI Technical Summary

Problems solved by technology

In the traditional terminal execution mode, if computing-intensive tasks are completely executed locally on the terminal, it may lead to excessive energy consumption of the terminal, long task running time, and failure to meet the operating requirements of complex applications.

On the contrary, if all the data of the terminal is sent to the cloud center for processing, although it can meet the precise calculation requirements of complex applications, it will greatly increase the load on the cloud center and network links, and also cause excessive communication energy consumption of the terminal

And because the distance between the terminal and the cloud is too long, it will cause a high transmission delay, which cannot meet the low-latency requirements of some terminal applications.

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