Service flow distribution optimization method and system for mobile edge computing

Abstract

The invention provides a mobile edge computing-oriented service flow distribution optimization method and system, and the method comprises the steps: carrying out the distribution processing of a service request flow if a service request cannot be established according to a conventional method and the flow of the service request has a minimum divisible unit, dividing one service request into a plurality of sub-service requests, and carrying out the optimization of the service flow distribution. And for each sub-service request, calculating a working path from the service request to the edge calculation server by adopting a K shortest path algorithm. After a working path is successfully selected, spectrum resource allocation is carried out on the path by adopting a spectrum allocation algorithm hit for the first time, and two constraint conditions of spectrum consistency and spectrum continuity need to be met at the same time. And when all the sub-service requests of one service request can be successfully connected, the service request is considered to be successfully connected. And the network computing resource and spectrum resource state information stored in the central controller is updated in real time.

Description

technical field [0001] The present invention relates to a method and a system for optimizing business flow distribution for mobile edge computing. Background technique [0002] With the rapid development of the Internet of Things and the technological advancement of various mobile devices, various new mobile applications have developed rapidly, and people's demand for high-quality service networks has become increasingly strong. The processing capability of existing mobile devices is getting stronger and stronger, but the amount of service requests that can be processed in a short period of time is still very limited. As a centralized computing mode, the cloud computing system has strong computing capabilities. Users can offload computing resources and transfer computing tasks to remote cloud servers for execution, which can effectively alleviate the problem of large demand for computing resources. However, transmitting a large number of computing tasks to cloud servers wil...

AI Technical Summary

Problems solved by technology

The allocation method of computing resource priority can effectively reduce the computing delay of business requests, but it cannot improve the computing power of nodes to process business requests, and the data processing delay still needs to be optimized; the allocation method of resource priority can improve the efficiency of nodes in processing business requests. speed, but the transmission delay may increase significantly

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