Scheduling method, device, equipment, storage medium and product for server cluster
By receiving and counting service requests, underutilized nodes were identified. Service type and Q-learning network strategies were used to optimize node allocation in the server cluster, solving the problem of resource waste and improving the service performance of the server cluster.
Patent Information
- Application Number
- CN202510073134.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-16
- Publication Date
- 2025-11-04
- Estimated Expiration
- 2045-01-16
AI Technical Summary
Traditional dynamic scheduling algorithms can easily lead to resource waste in server clusters because different users use different types of services and have different resource consumption, resulting in inappropriate allocation.
By receiving service requests and counting the number of service requests within a set time period, the latest underutilized nodes in the server cluster are identified. Based on the comparison results of service type and number of nodes, a target scheduling strategy is adopted to allocate service nodes to service requests, including a first target scheduling strategy based on service type and a second target scheduling strategy based on Q-learning network. The node allocation is optimized by combining the reinforcement learning algorithm of Q-learning network.
This approach optimizes service node allocation, reduces resource waste, and improves the service performance of server clusters by taking into account the differences in resource consumption among different service types.
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Abstract
Citation Information
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