Task scheduling method based on micro-cloud load balancing in wireless metropolitan area network

Abstract

The invention discloses a task scheduling method based on micro-cloud load balancing in a wireless metropolitan area network. According to the method, how to unload tasks and dispatch the tasks to reasonable micro-clouds is considered, so that a system delay is shortened. For solving the problem of optimization of response time of the micro-cloud tasks in a wireless metropolitan area network environment, an algorithm thought of micro-cloud load balancing is introduced, and a micro-cloud load balancing strategy is provided, thereby shortening average response time of the micro-cloud tasks as much as possible under the condition of meeting multiple constraints in the network. According to the strategy, unique characteristics of the wireless metropolitan area network and micro-cloud task unloading are fully considered; and firstly, the micro-clouds of the network are classified, then expected values of input / output task quantities of the micro-clouds are calculated, and finally minimum-cost maximum flow meeting the constraints is found in a flow network so as to execute all the tasks in the network. The method has positive effects in shortening user task response time and improving user experience.

Description

technical field [0001] The present invention relates to a task scheduling method in the field of edge computing, in particular to a task scheduling method based on wireless metropolitan area network micro-cloud load balancing. Background technique [0002] In recent years, with the development of mobile computing technology, mobile users can experience more application program functions on terminal devices. Portable mobile devices are limited in size, resulting in limited computing power, which is in conflict with the continued growth of application resources. The traditional method to solve the problem of lack of computing resources on mobile devices is to use the abundant computing resources of cloud servers. Mobile devices can reduce their workload and extend battery life by offloading computationally intensive tasks to the cloud. However, the physical distance between the mobile user and the remote cloud is too long, resulting in a long time delay, which causes the res...

AI Technical Summary

Problems solved by technology

If a micro-cloud is suddenly overloaded, the response time of tasks assigned to that micro-cloud will increase dramatically, causing user application response lag and seriously affecting user experience

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