Parallel scheduling system and method for distributed cluster load balancing

Abstract

The present invention discloses a parallel scheduling system and method for distributed cluster load balancing, relating to the technical field of load balancing scheduling, and characterized in that: by virtualizing an IP address to receive a user request message, master servers of all clusters exchange information with one another by sending a Hello packet, to select a cluster with the maximum idle value to bind the virtualized IP address, and the cluster sends a Statement packet to notify the whole network after binding; and a master server of the cluster looks up a task table and calculates data in an idle table and an interactive table according to a certain calculation method, to obtain an optimal response cluster, and pushes the request message to the optimal response cluster by sending a Push packet, so as to implement the load balancing in response to request messages among peer-to-peer clusters. The master server of the cluster is provided with four tables including a task table, an idle table, an interactive table and a calculation result table which are used for scheduling request messages, and four packets including a Hello packet, a Push packet, an Exchange packet and a Statement packet are used for maintaining relationships among the clusters.

Description

technical field [0001] The invention relates to the technical field of load balancing scheduling, in particular to a distributed cluster load balancing parallel scheduling system and method. Background technique [0002] The use of cluster technology solves the problems of the central processing unit of the system composed of a single server and the slow input and output processing speed. The wide application of cluster technology has indeed brought convenience and economic benefits to the majority of users. However, the problem of inter-cluster load balancing has not been well resolved, and often only partial load balancing has been achieved, and completely reasonable resource scheduling has not been achieved. In real life, it often happens that a central server is set up to manage the relationship between clusters to achieve load balancing, which inevitably increases overhead and easily causes single point of failure, resulting in the inability of clusters to efficiently ...

AI Technical Summary

Problems solved by technology

However, the problem of inter-cluster load balancing has not been well resolved, and often only partial load balancing has been achieved, and completely reasonable scheduling of resources has not been achieved.

In real life, it often happens that a central server is set up to manage the relationship between clusters to achieve load balancing, which inevitably increases overhead and easily causes single point of failure, resulting in the inability of clusters to efficiently respond to users request information for

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