Network Event Grouping via Graph Chordal Subgraph Analysis
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Solution Overview
Problem
Cloud computing systems face inefficiencies in managing large volumes of alerts and events, as existing methods fail to effectively group and prioritize these events, leading to information overload for network administrators.
Innovation Solution
A system and method that generates a graph based on historical event data, prunes edges with low weights to identify chordal subgraphs, and groups event types associated with these subgraphs, facilitating efficient event management and response by transmitting or displaying data specifying these groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all events are displayed individually to network administrators, then complete information is provided, but information overload occurs and response efficiency decreases
Solution Approach 1:
The patent merges multiple related events into grouped representations that preserve the essential information of each event while presenting them as cohesive units. Events are grouped based on relationships detected through graph analysis, allowing administrators to view related events together without being overwhelmed by individual event details, thus maintaining information completeness while improving response efficiency
Solution Approach 2:
The patent segments the large volume of events into meaningful groups based on their relationships. By dividing events into distinct groups with clear boundaries and characteristics, the system makes the information more manageable and easier to process, allowing administrators to focus on group-level patterns while still accessing individual event details when needed
2Measurement precision
If graph processing includes all edges to ensure complete event relationships, then relationship accuracy is maintained, but processing time and computational complexity increase
Solution Approach 1:
The patent extracts and removes edges from the graph that have weights below a predetermined threshold, eliminating weak or insignificant relationships from the processing. This extraction of relevant information allows the system to focus computational resources on strong relationships that are more likely to represent meaningful event connections, maintaining accuracy for important relationships while reducing overall processing time
Solution Approach 2:
The patent applies a weight threshold parameter to filter edges in the graph. By changing the parameter that determines which edges are processed (from including all edges to including only edges above a threshold weight), the system optimizes the balance between relationship accuracy and processing efficiency, removing edges that contribute minimally to relationship accuracy while significantly reducing processing burden
Data Source
AI summary
Systems and methods are disclosed for network event grouping. For example, methods may include generating a graph including vertices and edges, wherein at least one of the vertices is associated with an event type from a set of event types and wherein at least one of the edges is associated with a weight; removing, based on an associated weight and a first threshold, one or more edges from the graph; determining, after removing the one or more edges from the graph, whether the graph is chordal; responsive to determining that the graph is chordal, identifying a connected subgraph within the graph; determining a group of event types to include event types that are associated with vertices in the identified connected subgraph; and transmitting, storing, or displaying data specifying the group of event types.