Metric Agents for Real-Time GPU Metrics Collection
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Solution Overview
Problem
Existing techniques for collecting metrics related to computing workloads are inefficient and lack real-time capabilities, making it difficult to optimize resource utilization and troubleshoot performance issues effectively.
Innovation Solution
A framework that employs metric agents on each node to collect and store workload and system metrics, which are then transmitted to a remote analytics service for analysis, using a token-based authentication system and orchestration services like Kubernetes for efficient deployment and scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing techniques are used to collect metrics, then resource utilization can be monitored, but real-time capabilities are lacking and optimization is inefficient
Solution Approach 1:
The system segments the metrics collection function by deploying metric agents on individual nodes rather than using centralized collection. Each node independently collects and stores its own metrics locally, enabling parallel operations across the distributed system and achieving real-time capabilities without centralized bottlenecks.
Solution Approach 2:
The patent introduces metric agents as intermediary components that reside on each node and facilitate local metrics collection and storage. These agents act as mediators between the computing workloads and the monitoring system, enabling efficient real-time data capture without direct intervention from central monitoring infrastructure.
2Adaptability or versatility
If centralized metrics collection is used, then data can be aggregated, but scalability and deployment efficiency are reduced
Solution Approach 1:
The architecture divides the monitoring system into independent node-level agents that operate autonomously. This segmentation eliminates the need for complex centralized deployment and allows the system to scale by simply adding more nodes with identical agent configurations, significantly improving adaptability and reducing deployment complexity.
Solution Approach 2:
Each node's metric agent independently collects, stores, and manages its own metrics without requiring external coordination or complex centralized management. This self-service approach simplifies the overall system architecture and enables easy scaling across distributed environments.
Data Source
AI summary
Apparatuses, systems, and techniques to obtain metric data of a computing resource service provider. In at least one embodiment, metric data of one or more graphics processing unit (GPUs) is caused to be obtained from the one or more GPUs in an order from newest to oldest.