Server Metrics Collection with Load-Based Throttling
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Solution Overview
Problem
Existing technologies fail to address the challenge of minimizing the resource strain on servers due to continuous metrics collection, particularly in high-load scenarios, which impacts performance and efficiency, by dynamically adjusting the metrics collection based on server load.
Innovation Solution
Implement a configuration file that sets CPU load thresholds for metrics collection, where agents check the CPU load before collecting metrics, and if above the threshold, they enter a backoff loop to resume collection when load decreases, ensuring critical metrics are collected only when server resources are available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous metrics collection is performed on servers, then monitoring precision and system health insights are improved, but server performance and resource availability deteriorate due to added load
Solution Approach 1:
The patent implements dynamic metrics collection where the monitoring system adjusts its behavior based on current server load conditions. Agents continuously monitor performance metrics and dynamically modify collection frequency, suspending or reducing metrics collection when load thresholds are exceeded, and resuming normal collection when load decreases. This dynamic adaptation resolves the contradiction by making monitoring precision variable rather than fixed, allowing the system to maintain server performance while still providing adequate monitoring under varying conditions.
Solution Approach 2:
The system changes the parameter of metrics collection frequency based on server load conditions. When load exceeds defined thresholds, the system modifies the collection parameter from continuous to reduced-frequency or suspended collection. This parameter change allows the system to balance monitoring needs against performance requirements, resolving the contradiction between measurement precision and server productivity by making the collection intensity adaptive to current system state.
2Reliability
If metrics collection frequency is increased to capture more system state details, then monitoring reliability is improved, but resource consumption and server strain worsen
Solution Approach 1:
The patent implements dynamic adjustment of metrics collection based on real-time server load monitoring. When load thresholds are exceeded, the system dynamically reduces collection frequency or suspends non-critical metrics collection, thereby reducing resource consumption while maintaining monitoring reliability for critical metrics. This dynamic behavior resolves the contradiction by making resource consumption adaptive rather than constant.
Solution Approach 2:
The system applies partial action by selectively collecting only critical metrics when load is high, rather than collecting all metrics continuously. The patent allows full metrics collection when resources are abundant, but transitions to partial collection of essential metrics when load exceeds thresholds. This partial action approach maintains monitoring reliability for critical system health indicators while reducing overall resource consumption during high-load periods.
Data Source
AI summary
Technology disclosed herein includes systems and methods for collecting server metrics. More specifically, systems and methods for performing dynamic metric collection are disclosed in which the metrics collected are throttled based on server load. In an embodiment of the technology, an agent on a server identifies a metric to collect and determines if the current processing load on the server is above a threshold for the metric. If the processing load is below the threshold, the agent collects the metric. If the processing load is above the threshold, the agent does not collect the metric. Load thresholds may differ between metrics based on how critical the metric is defined to be.