Multidimensional Heat Maps for Cloud Performance Diagnosis
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Solution Overview
Problem
Cloud computing resources face challenges in managing and optimizing performance due to varying workload demands from users, which can lead to issues such as latency and resource utilization inefficiencies, making it difficult for system administrators to diagnose and resolve performance problems in real-time.
Innovation Solution
The generation of multidimensional heat maps from event data using performance characteristics, constraints, and hue assignments allows for visual representation and analysis of computing resource performance, enabling system administrators to identify and address performance issues across cloud-based systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to track computing resource performance, then system administrators can monitor resource usage, but they cannot effectively diagnose performance problems in real-time due to the complexity and volume of data
Solution Approach 1:
The patent segments the complex performance data into multiple dimensions (time, resource type, workload characteristic) and represents them as discrete bins in a heat map. Each bin contains aggregated event data, transforming overwhelming raw data into manageable, visually distinct units that can be quickly analyzed for performance issues.
Solution Approach 2:
The patent transitions from traditional two-dimensional monitoring displays to a multidimensional heat map representation that encodes multiple data dimensions (time, resource type, performance metric) into a single visual structure. Color intensity and positional encoding add additional dimensions, enabling comprehensive performance diagnosis in a single view rather than requiring multiple separate monitors.
2Loss of information
If detailed event data is collected for all computing resources, then comprehensive performance analysis is possible, but real-time visualization and analysis become difficult due to the volume of data
Solution Approach 1:
The patent merges multiple event data points that fall within the same time window, resource type, and performance metric range into single aggregated bins. This consolidation preserves the complete set of observed events while representing them compactly through color intensity in the heat map, enabling real-time visualization without information loss.
Solution Approach 2:
The patent uses color intensity variations in the heat map to encode the volume and characteristics of event data. Different color intensities represent different concentrations of events in each bin, allowing system administrators to quickly identify areas of high activity or anomaly without examining individual event records, thus maintaining data completeness while improving analysis ease.
3Reliability
If traditional monitoring displays are used, then resource usage can be tracked, but performance issues cannot be detected and resolved promptly due to lack of real-time visualization
Solution Approach 1:
The patent pre-aggregates event data into time-based bins and continuously updates the heat map representation as new events occur. This preliminary organization of data into visual bins allows performance issues to be detected immediately when they occur, rather than requiring post-processing analysis, thus reducing issue detection time while maintaining system reliability.
Solution Approach 2:
The heat map provides immediate visual feedback about system performance status through color-coded bins. When performance degradation occurs, the corresponding bins change appearance, giving system administrators real-time feedback about the nature and location of issues, enabling prompt response and resolution while maintaining system stability.
Data Source
AI summary
Systems, methods, and media for generating heat maps of event data are provided herein. Methods may include gathering instances of event data according to a performance characteristic, discretely decomposing the instances by applying at least one constraint to the instances, assigning a hue to each instance, the hue being associated with the at least one constraint, and generating a heat map that includes representations of the instances, wherein each representation includes the hue associated with the at least one constraint to which the instance has been assigned.