Distributed System Metric Event Grouping via Consistent Hashing
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Solution Overview
Problem
Current cloud monitoring systems require manual filtering and sorting of metric events to determine system behavior, which is inefficient and time-consuming, especially when dealing with large volumes of data from distributed computing systems.
Innovation Solution
A method that evaluates metric events from monitored elements based on tags and conditions by applying declared group functions to admit, evict, or maintain membership in declared groups, using consistent hashing for routing and updating group memberships, and logging changes for audit purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual filtering and sorting of metric events is used to determine system behavior, then monitoring accuracy can be maintained, but efficiency and time consumption deteriorate significantly when dealing with large volumes of data
Solution Approach 1:
The patent applies preliminary action by pre-defining groups of monitored elements and their associated metric events before monitoring occurs. The system pre-configures grouping criteria and relationships, so that when metric events are generated, elements are automatically assigned to groups without requiring real-time manual filtering or sorting. This advance preparation eliminates the time-consuming manual data processing step while maintaining accurate monitoring of system behavior.
2Productivity
If automated grouping of monitored elements is implemented, then processing efficiency improves, but system complexity increases due to group management overhead
Solution Approach 1:
The patent applies universality by creating a multi-functional grouping mechanism that handles multiple operations through a single unified structure. The pre-defined groups serve multiple purposes: they organize monitored elements, categorize metric events, enable bulk operations on related elements, and provide a framework for automated response actions. This universal grouping approach consolidates what would otherwise require separate management systems, reducing overall complexity while maintaining high processing efficiency.
3Measurement precision
If real-time evaluation of metric events is performed, then system monitoring accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies the extraction principle by separating the evaluation logic for different types of metric events and organizing them into distinct pre-defined groups. Instead of evaluating all metric events uniformly in real-time, the system extracts and processes only those events relevant to each specific group based on pre-configured criteria. This selective extraction reduces the computational burden of real-time evaluation while maintaining accurate detection of system behavior changes by focusing resources on critical events.
Data Source
AI summary
In an embodiment, a method for monitoring elements of a distributed computing system is disclosed. In the embodiment, the method involves evaluating a metric event from a monitored element based on at least one of tags and conditions of the monitored element by applying declared group functions corresponding to declared groups over the metric event and at least one of admitting the monitored element into membership of a declared group, evicting the monitored element from membership of a declared group, and maintaining membership of the monitored element in a declared group based on the evaluation of the metric event.