Temporal Network Dependency Visualization for Service Diagnosis
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Solution Overview
Problem
Conventional tools for visualizing dependencies in data networks are inadequate, as they cannot represent chains of dependencies across multiple services or applications, lacking temporal aspects and failing to provide a comprehensive view necessary for diagnosing network disruptions or scheduling maintenance without disrupting operations.
Innovation Solution
A method and apparatus for generating a visualization of service dependencies in data networks, using a network management device to create node indications and service path indications, with a graph processing module that assigns colors to interactions based on time, enabling a visual representation of multiple node interactions over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional visualization tools are used to represent service dependencies, then the implementation is simple and tools are readily available, but the tools cannot represent chains of dependencies across multiple services or applications and lack temporal aspects
Solution Approach 1:
The patent adds a temporal dimension to the visualization by capturing service dependencies over time periods rather than at a single instant. This allows the system to represent chains of dependencies across multiple services and applications while maintaining their temporal relationships, resolving the limitation of conventional tools that only show instantaneous snapshots.
Solution Approach 2:
The system nests multiple levels of dependency information within the visualization, where service dependencies are nested within time periods, and individual interactions are nested within service paths. This hierarchical nesting allows comprehensive representation of complex dependency chains while organizing the information in a manageable structure.
2Reliability
If conventional tools capture only instantaneous snapshots of network conditions, then the data processing is simple, but the tools are insufficient to properly diagnose network disruptions or schedule maintenance
Solution Approach 1:
The system performs preliminary actions by capturing and storing service dependency information over time periods before disruptions occur. This allows network administrators to have pre-collected data about normal operational patterns and dependency relationships, which can then be used to quickly diagnose disruptions without needing to collect data during the actual incident.
Solution Approach 2:
The system maintains continuous monitoring and capture of service dependencies over time periods rather than taking discrete instantaneous snapshots. This continuous action ensures that temporal patterns and chains of dependencies are preserved, enabling more reliable diagnosis of network disruptions while the data collection occurs in the background without interrupting normal network operations.
3Ease of operation
If comprehensive temporal visualization of service dependencies is implemented, then network administrators can effectively diagnose disruptions and schedule maintenance, but the system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential dependency relationships and temporal patterns from the comprehensive network data, rather than attempting to visualize and process all possible information. The graph processing module focuses on extracting service dependencies, time period associations, and interaction patterns, filtering out redundant data to maintain manageable complexity while providing comprehensive diagnostic capabilities.
Data Source
AI summary
Example methods and systems for mapping network service and/or application dependencies are provided. Some examples may visualize a large, complex network of network services and/or applications (e.g., Internet services and applications) and their dependencies over time. Each service (or application) may be represented as a node and the visualization may present information regarding the relationships among services and/or applications using directed edges (or lines) with varying thickness, colors, and/or line-styles depending on network data.