Cloud Asset Observability Dashboard for Complex System Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional monitoring systems fail to provide a comprehensive understanding of system performance in complex, dynamic cloud-based environments, lacking a system-level view and being insufficient for identifying unknown faults or errors in modern applications with complex architectures.
Innovation Solution
A system and method for implementing observability by stitching together various metrics from different sources using processors and memory to generate a dashboard that provides a holistic understanding of asset performance, including product, infrastructure, and service levels, with threshold comparisons to alert users of performance deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional monitoring systems are used to track known issues, then basic performance monitoring is achieved, but system-level view and understanding of complex architectures are not provided
Solution Approach 1:
The system segments monitoring into multiple levels: application level, infrastructure level, and service level. Each level monitors specific metrics and aggregates them to provide a comprehensive system-level view, resolving the contradiction between basic monitoring and complex architecture understanding
Solution Approach 2:
The system merges data from multiple sources including application logs, infrastructure metrics, and service performance data into a unified dashboard. This integration provides a holistic system-level view that connects previously siloed monitoring information across complex architectures
2Ease of operation
If pre-configured dashboards are used for data visualization, then basic metric monitoring is achieved, but cumulative understanding and combination of different logs or metrics are not provided
Solution Approach 1:
The dashboard system is designed to universally handle multiple types of data including logs, metrics, and performance data from various sources. It provides multi-functional capabilities to aggregate, correlate, and visualize different data types together, enabling cumulative understanding while maintaining ease of operation through a unified interface
3Productivity
If manual data collection and script writing are performed, then limited metric monitoring is achieved, but efficiency and complete user observability are not provided
Solution Approach 1:
The system implements self-service through automated data collection from multiple sources, automatic metric aggregation, and intelligent dashboard generation. It eliminates manual data collection and script writing by automatically provisioning and managing monitoring infrastructure, thereby improving productivity while ensuring complete and reliable user observability
Data Source
AI summary
A system for metric collection and performance analysis of an asset in a network having one or more processors, a memory, and one or more programs stored in a memory, the one or more programs comprising instructions configured to identify a plurality of products associated with the asset, wherein each product is configured to generate one or more metrics associated with performance of said product, retrieve one or more metrics associated with one or more products, determine a key performance indicator for one or more of the metrics, assign a threshold value to one or more key performance indicator to determine performance of the one or more products, transmit a prompt to a user, when the key performance indicator associated with one or more products is below the threshold value and generate a dashboard, said dashboard presenting a report corresponding to one or more key performance indicator for associated metrics and the threshold value.