Dynamic Metric Management in Multi-Cluster Computing
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Solution Overview
Problem
Distributed computing environments face challenges in efficiently managing metrics across multi-cloud and edge deployments, leading to increased overhead costs and resource inefficiencies due to static monitoring approaches.
Innovation Solution
The implementation of dynamic metric management techniques that adjust metric collection frequency and resource allocation based on changes in application deployment, service level objectives, cluster resource availability, and monitoring overhead budgets, utilizing metric importance analysis to prioritize data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static monitoring approaches are used to collect metrics from distributed computing environments, then comprehensive observability is maintained, but overhead costs and resource consumption increase
Solution Approach 1:
The patent implements dynamic metric collection frequency adjustment based on computed importance values. The system continuously adapts the collection frequency of different metrics according to their computed importance, transitioning from static to dynamic monitoring. This allows the system to maintain comprehensive observability for critical metrics while reducing or suspending collection of less important metrics, thereby resolving the contradiction between maintaining reliability and reducing energy loss.
Solution Approach 2:
The system changes the parameter of metric collection frequency based on computed importance values. By calculating importance scores for different metrics and adjusting their collection frequencies accordingly, the system optimizes resource utilization while maintaining necessary observability. This parameter change approach allows critical metrics to be monitored frequently while non-critical metrics are monitored less frequently, reducing overall overhead costs.
2Measurement precision
If metric collection frequency is increased to maximize event detection, then detection capability is improved, but bandwidth consumption and resource overhead increase
Solution Approach 1:
The patent applies different metric collection frequencies to different metrics based on their local importance characteristics. Instead of using a uniform collection frequency for all metrics, the system computes importance values for each metric and applies localized collection strategies. Critical metrics with high importance values receive frequent collection to maximize event detection, while non-critical metrics receive less frequent collection to reduce bandwidth consumption, thus resolving the contradiction through differentiated local quality.
3Ease of operation
If uniform metric collection frequency is applied across all metrics, then implementation simplicity is maintained, but resource efficiency decreases
Solution Approach 1:
The system dynamically changes the collection frequency parameter for different metrics based on computed importance values. This allows the system to move from a simple uniform collection approach to a differentiated approach where each metric has its own optimized collection frequency. The importance computation mechanism automates this differentiation, maintaining ease of operation while significantly improving resource efficiency by avoiding unnecessary collection of non-critical metrics.
Data Source
AI summary
Metric management techniques in a multi-cluster computing environment are disclosed. For example, a method obtains a set of metrics collected from one or more computing clusters of a distributed computing environment, wherein the set of metrics are associated with one or more processes that are executable on the one or more computing clusters. The method computes one or more metric importance values for the set of metrics based on one or more processing criteria. The method sends the one or more metric importance values to at least one computing cluster of the one or more computing clusters to enable the at least one computing cluster to adapt a collection frequency of at least one metric of the set of metrics. Such adaptation, by way of example, can be based on the metric importance and resource availability.