Resource Redeployment via ML-Based Host Node Selection
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Solution Overview
Problem
In large-scale networked systems, managing computing resources to optimize performance is challenging due to high inter-cluster communication frequencies and network latencies, which can impact application performance. Manual intervention is often required, making it difficult to efficiently redeploy resources as the system size grows.
Innovation Solution
A resource management system that monitors performance parameters such as communication frequency and network latency across multiple host-computing nodes. Using machine-learning models, the system determines candidate host-computing nodes for redeployment based on performance data, thereby optimizing resource allocation and reducing inter-cluster communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used to manage computing resources, then resource allocation can be controlled, but system complexity increases and efficiency decreases as system size grows
Solution Approach 1:
The system employs machine learning models that automatically monitor performance parameters, determine candidate host-computing nodes, and execute redeployment decisions without manual intervention. The system serves itself by continuously optimizing resource allocation based on real-time performance data, eliminating the need for human operators to manage complex redeployment scenarios in large-scale networks.
Solution Approach 2:
Manual mechanical intervention in resource management is replaced with automated machine learning-based decision-making systems. The ML models process performance parameters and automatically determine optimal redeployment strategies, substituting human operational complexity with algorithmic efficiency that scales seamlessly with system size.
2Quantity of substance
If computing resources are deployed across multiple clusters, then system capacity increases, but inter-cluster communication frequency increases causing performance degradation
Solution Approach 1:
The system continuously monitors performance parameters including inter-cluster communication frequencies and network latencies. Based on this feedback, the machine learning model automatically determines optimal redeployment strategies that reduce harmful communication patterns while maintaining system capacity. The feedback loop enables dynamic adjustment of resource placement to optimize both capacity and performance.
Solution Approach 2:
The system changes the deployment parameters of computing resources by redeploying them from suboptimal host-computing nodes to better-suited nodes. This parameter change in resource placement reduces inter-cluster communication frequencies and network latencies, thereby improving application performance while preserving the distributed system's capacity.
3Speed
If redeployment decisions are made without performance data, then decision speed increases, but resource allocation optimization decreases
Solution Approach 1:
The system performs preliminary analysis of performance parameters continuously in the background, so when redeployment decisions are needed, the machine learning model already has processed data ready for rapid decision-making. This preliminary action enables both fast decision speed and high optimization precision by having performance insights prepared in advance.
Data Source
AI summary
Examples described herein relate to method, resource management system, and non-transitory machine-readable medium for redeploying a computing resource. Data related to a performance parameter corresponding to a plurality of computing resources deployed on a plurality of host-computing nodes may be received. The performance parameter is associated with one or both of: communication between computing resources of the plurality of computing resources, or communication of the plurality of computing resources with a network device. Further, for a computing resource of the plurality of computing resources, a candidate host-computing node is determined from the plurality of host-computing nodes based on the data related to the performance parameter and the computing resource may be redeployed on the candidate host-computing node.