Load balancing method, device, electronic device and computer program product
By real-time analysis of the load conditions of each node in a multi-single node cluster environment, determining the allocation strategy and automatically allocating tasks, the problem of difficult load balancing in the existing technology is solved, and the task processing efficiency of the cluster is improved.
CN119759553APending Publication Date: 2025-04-04SHENZHEN JIUNIU YIMAO INTELLIGENT IOT TECH CO LTD
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Patent Information
- Application Number
- CN202411813681.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-10
- Publication Date
- 2025-04-04
AI Technical Summary
Technical Problem
In a multi-single-node cluster environment, it is difficult for the existing technology to achieve effective load balancing, resulting in uneven task allocation and affecting the cluster's task processing efficiency.
Method used
By obtaining real-time node status data, analyzing the load status of each cluster node, determining the allocation strategy, and using this strategy to automatically assign tasks.
Benefits of technology
It realizes accurate timely allocation of assigned tasks, improves the task processing efficiency of the cluster, and avoids resource consumption problems caused by the server simultaneous load balancing and task execution.
✦ Generated by Eureka AI based on patent content.
Abstract
The present application is applicable to the field of load balancing technology, and provides a load balancing method, device, electronic device and computer program product. The method includes obtaining real-time node status data, wherein the node status data includes the running status data of multiple cluster nodes associated with the client, analyzing the load of each cluster node according to the real-time node status data, obtaining the real-time node load analysis results corresponding to each cluster node, determining the allocation strategy according to the real-time node load analysis results corresponding to each cluster node, and using the allocation strategy to allocate tasks to be allocated. The present application can improve the task processing efficiency of the cluster.
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