Infrastructure Fault Visualization for Queue-Worker Impact Monitoring
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Solution Overview
Problem
In cloud computing environments, monitoring the health of infrastructure entities with a many-to-many relationship between queues and workers is complex, making it difficult to determine and communicate the impact of faults or performance degradation on the overall system effectively.
Innovation Solution
A visualization tool is developed to present the health of multiple infrastructure entities, using a grid layout for queues and a circular radial layout for workers, with interactive features to highlight impacted queues and workers, and dynamic updates for real-time events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used to track infrastructure entities with many-to-many relationships, then complete monitoring coverage is achieved, but the complexity of determining and communicating fault impact increases significantly
Solution Approach 1:
The visualization divides the complex infrastructure into distinct segments: queues are displayed in a grid layout while workers are arranged in a circular radial layout. This spatial segmentation allows operators to mentally separate different entity types and their relationships, making the many-to-many connections more manageable and easier to analyze for fault impact.
Solution Approach 2:
The patent transforms the abstract many-to-many relationship data into a two-dimensional visual space. By mapping queues to grid positions and workers to circular positions with connecting lines, the system adds a spatial dimension that makes complex relationships intuitively understandable, reducing the cognitive complexity of fault impact analysis.
2Loss of information
If detailed monitoring data for all infrastructure entities is displayed, then complete system visibility is achieved, but the difficulty of identifying critical information increases
Solution Approach 1:
The visualization employs color coding to represent different health states of queues and workers. Critical information such as faulty workers or impacted queues is highlighted through distinct colors, allowing operators to quickly identify problems without being overwhelmed by the volume of monitoring data. This visual encoding transforms detailed data into easily interpretable signals.
Solution Approach 2:
The system applies different visual properties to different parts of the visualization based on their state. Healthy entities have one visual appearance while problematic entities have another, creating local quality variations that guide operator attention to critical areas. This allows complete information display while making critical information stand out through localized visual characteristics.
3Measurement precision
If real-time updates are implemented to reflect current system state, then monitoring accuracy is improved, but the computational resources and system complexity increase
Solution Approach 1:
The visualization implements real-time feedback by automatically updating the display when infrastructure entities change state. When a worker becomes faulty or a queue is impacted, the visualization immediately reflects these changes through dynamic updates. This feedback mechanism maintains measurement precision without requiring complex manual intervention, as the system self-updates based on monitored events.
Data Source
AI summary
An infrastructure monitor receives an indication of a fault on a first host computer of a plurality of host computers, wherein each of the plurality of host computers is associated with a different subset of a plurality of queues, and wherein each of the plurality of queues are serviced by a different subset of the plurality of host computers. The monitor identifies a first subset of the plurality of queues associated with the first host computer and determines a workload present on the first subset of the plurality of queues. The monitor further generates a single visualization to provide the indication of the fault on the first host computer, the first subset of the plurality of queues impacted by the fault, and the workload present on the first subset of the plurality of queues and causes presentation of the single visualization.