Dynamic Compute Node Scaling for Data Query Concurrency
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Solution Overview
Problem
High concurrency in data query requests leads to CPU, memory, and network bandwidth abnormalities, resulting in query timeouts or failures in open analytics systems, particularly in map and image data query scenarios.
Innovation Solution
A data query method that dynamically adjusts compute nodes in a resource pool based on resource overheads and feature information of query requests, distributing requests to allocation groups corresponding to different resource sub-pools to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the compute node processes multiple query requests simultaneously to improve concurrency handling, then the query response speed is improved, but the CPU resource, memory resource and network bandwidth become abnormal leading to query timeout or failure
Solution Approach 1:
The patent implements dynamic resource allocation by adjusting the number of compute nodes in the resource pool based on real-time resource overheads and query request characteristics. This dynamic adjustment allows the system to scale computing capacity up or down automatically, preventing resource exhaustion during high concurrency while maintaining efficient resource utilization during lower load periods, thus resolving the contradiction between processing throughput and system reliability
Solution Approach 2:
The system changes key operational parameters including the number of active compute nodes, resource allocation ratios, and scheduling policies based on monitored resource overheads. By dynamically adjusting these parameters, the system optimizes the balance between handling high concurrency queries and maintaining stable CPU, memory, and network resource levels, preventing timeout and failure conditions
2Productivity
If the resource pool size is increased to handle high concurrency queries, then the query processing capacity is improved, but the resource utilization efficiency decreases due to idle compute nodes
Solution Approach 1:
The patent employs dynamic resource allocation where compute nodes are adjusted in real-time based on actual workload and resource overheads. During high concurrency periods, additional compute nodes are activated to handle the load; during low activity periods, nodes are deactivated or placed in standby mode. This dynamic approach ensures high processing capacity when needed while minimizing resource waste during idle periods
Solution Approach 2:
The system implements self-service resource management through automatic monitoring of resource overheads and autonomous adjustment of compute node allocation. The resource pool self-regulates its capacity based on query request patterns and resource consumption levels, eliminating the need for manual intervention and optimizing the balance between available capacity and actual utilization efficiency
Data Source
AI summary
A method including obtaining resource overheads according to feature information of a received query request; according to the resource overheads and a compute node resource, dynamically adjusting a compute node in a resource pool; and querying, by using the compute node, data corresponding to the query request. A compute node in a resource pool may be dynamically adjusted, so that the compute node in the resource pool may process all the received query requests, and therefore, the processing efficiency and a resource utilization rate of the compute node are more effectively improved, such that the compute node may more efficiently perform parallel processing on the multiple query requests, and the utilization rates of a CPU resource, a memory resource and a network bandwidth resource are increased, thus achieving better effect from the perspectives of overall computing resource and user query load and improving the usage experience of a user.