Aggregation Nodes for Distributed Metric Data Collection
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Solution Overview
Problem
The complexity of modern distributed computing systems generates vast amounts of metric data, leading to a computational bottleneck in collection, storage, and processing, hindering the evolution and management of these systems.
Innovation Solution
The implementation of a graph-like representation using aggregation nodes to collect and process population metrics for system components, including those within containers and virtual machines, facilitates efficient data aggregation and outlier detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to collect and store metric data for each component individually, then complete component-level monitoring is achieved, but the volume of data generated and stored becomes excessively large, creating a computational bottleneck
Solution Approach 1:
The patent combines multiple individual component metrics into population-level metrics by aggregating data from multiple components of the same type. Instead of storing and processing each component's metrics separately, the system merges them into consolidated population metrics that represent the collective state of component groups, thereby reducing data volume while preserving monitoring capability.
Solution Approach 2:
The patent introduces a new dimension of analysis by creating population metrics that operate at an aggregate level above individual components. This dimensional shift allows the system to view and analyze component behavior collectively, transforming the data structure from individual-component granularity to population-level granularity, which reduces the overall data volume while maintaining monitoring effectiveness.
2Productivity
If detailed metric data is collected and stored for all components, then comprehensive system analysis is enabled, but the computational bandwidth required for processing increases exponentially
Solution Approach 1:
The patent extracts only the essential aggregate information from individual component metrics by creating population metrics. Instead of processing all detailed component-level data, the system extracts and retains only the consolidated population-level characteristics that are necessary for system-wide analysis, thereby reducing computational bandwidth requirements while maintaining analytical capability.
Solution Approach 2:
The patent segments the monitoring system into two distinct levels: individual component monitoring and population-level aggregation. By separating these functions, the system can collect detailed component data when needed while primarily processing and storing aggregated population metrics, which significantly reduces the computational bandwidth required for ongoing system analysis.
3Quantity of substance
If population metrics are aggregated from multiple components, then data volume is reduced, but the complexity of the data structure increases due to the graph-like representation
Solution Approach 1:
The patent introduces population metrics as intermediary structures that mediate between individual component metrics and system-wide analysis. These population metrics serve as a intermediate layer that simplifies the data structure by providing a standardized aggregate representation, making the overall system easier to manage despite the graph-like relationships between components.
Data Source
AI summary
The current document is directed to methods and subsystems within computing systems, including distributed computing systems, that collect, store, process, and analyze population metrics for types and classes of system components, including components of distributed applications executing within containers, virtual machines, and other execution environments. In a described implementation, a graph-like representation of the configuration and state of a computer system included aggregation nodes that collect metric data for a set of multiple object nodes and that collect metric data that represents the members of the set over a monitoring time interval. Population metrics are monitored, in certain implementations, to detect outlier members of an aggregation.