Distributed Mobile Edge Computing Resource Scheduling Method and System for Delay Sensitive Tasks

Abstract

The invention discloses a method and system for dispatching distributed mobile edge computing resources for delay-sensitive tasks. The method includes the following steps: predicting the access source edge node of the edge computing network within the maximum delay of the delay-sensitive task to be processed by the vehicle and converging edge nodes; the control layer calculates the data transmission cost to other edge nodes in the network from the resource information table with the source edge node and the converging edge node as the target node; extracts the task processing cost of each edge node from the resource information table, Sum the data transmission cost respectively to obtain the total cost, put the edge nodes into the resource queue according to the total cost; allocate the delay-sensitive tasks to be processed to the k edge nodes with the lowest total cost in the resource queue for processing, and complete the scheduling . The present invention sorts the edge nodes and distributes calculation tasks to several edge nodes, thereby minimizing calculation delay and minimizing task processing costs.

Description

technical field [0001] The invention belongs to the technical field of computer communication, and in particular relates to a resource scheduling method and system for distributed mobile edge computing of delay-sensitive tasks. Background technique [0002] The Internet of Vehicles (IoV), which mainly consists of connected vehicles, roadside infrastructure, faces many challenges, mainly due to high mobility, limitations of on-board resources and computing power, etc.; (Quality of Experience, QoE) at the same time, it is difficult to complete computing-intensive applications locally. Only by determining an intelligent computing strategy including data computing and data transmission strategies and deploying them seamlessly can delay-sensitive vehicle services be provided. This requires that computing strategies must consider various factors (network delay, bandwidth, computing power, mobility characteristics, etc.) and provide delay-sensitive in-vehicle services at low cost. ...

AI Technical Summary

Problems solved by technology

However, this existing algorithm does not take into account the hotspot areas in the entire network. If the task request of a local hotspot area is too large, the algorithm needs to be iterated continuously to meet the user's QoE request, and the cost is relatively high.

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