Server load balancing method based on dual-objective optimization

Abstract

The server load balancing method based on dual-objective optimization comprises the following steps: S1, determining the number of servers to be processed in a one-time request distribution process, and acquiring the number of task requests by adopting a sliding window according to the number of concurrent requests in a time node; s2, obtaining real-time state information of the servers, wherein the real-time state information comprises the comprehensive processing capacity of node servers in a quantitative cluster and the resource usage amount of each server at the arrival moment of a task request; s3, coding the relationship between the task request and the processing server by adopting a 0-1 matrix; s4, constructing a target function of a cluster load request allocation optimization problem; s5, performing iterative optimization solution on the proposed problem by using a cuckoo search algorithm, and outputting a coding solution set; s6, outputting a proper coding scheme; and S7, decoding the scheme, finding a final relationship between the task request and the processing server, and randomly selecting a coding scheme by a scheduling mechanism to perform task scheduling. According to the invention, the problem of load balanced distribution in the cluster of the server can be solved, and the whole solution is more efficient and accurate.

Description

technical field [0001] The invention belongs to the field of server cluster load balancing, in particular to a server load balancing method based on dual-objective optimization. Background technique [0002] In recent years, the rapid development of network intelligence is mainly reflected in the rapid growth of Internet user visits and network traffic. According to relevant survey results, as of June 2020, my country's network users have reached 940 million. The development of the network is closely related to people's lives, especially in other IoT industries such as smart home, smart enterprise, and smart high-speed that are gradually emerging. In addition, the popularization of terminal devices such as mobile phones, ipads and computers is also the reason for the rapid development of the network. Then the problem that follows is that when too many users request to access the same resource at the same time, if the server cannot process the requests in a timely manner, it ...

AI Technical Summary

Problems solved by technology

With the continuous development of cluster technology, the traditional load balancing algorithm can no longer meet the needs of users for server-side load balancing. More and more scholars combine it with optimization combination, neural network, meta-heuristic algorithm, etc. for research. To achieve more complex load balancing effects

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