Iterative Entity Discovery for Automated System Instrumentation
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Solution Overview
Problem
In modern data centers and computing environments, analyzing and searching massive quantities of machine data generated from diverse sources is time-consuming and challenging due to the varied types and formats of data, making it difficult to efficiently instrument and monitor all entities within the system, especially with the presence of uninstrumented entities.
Innovation Solution
An event-based data intake and query system with a late-binding schema is employed, allowing for flexible data extraction and instrumentation of entities through iterative discovery and installation of monitoring agents, enabling automatic or guided installation of monitoring agents in uninstrumented entities connected to instrumented ones, and utilizing a monitoring and analytics system to collect, analyze, and report operational data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual instrumentation is performed for each data system, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The system performs self-instrumentation by automatically discovering entities through telemetry data analysis and installing monitoring agents without requiring manual intervention for each entity. The automated entity discovery process analyzes existing telemetry data to identify uninstrumented entities, determines their characteristics, and automatically installs appropriate monitoring agents, enabling the system to instrument itself efficiently.
Solution Approach 2:
The system performs preliminary actions by analyzing telemetry data from instrumented entities to discover uninstrumented entities before they need to be monitored. The automated discovery process proactively identifies entities, determines their characteristics, and prepares instrumentation plans in advance, reducing the time required when instrumentation is actually needed.
2Measurement precision
If comprehensive instrumentation of all entities is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system automatically discovers entities and determines their instrumentation requirements through telemetry data analysis, eliminating the need for manual inventory and classification. The automated process identifies uninstrumented entities, analyzes their characteristics from telemetry data, and selects appropriate monitoring agents, reducing the complexity of managing comprehensive instrumentation across diverse entities.
Solution Approach 2:
The system segments the instrumentation process into distinct automated phases: entity discovery through telemetry analysis, characteristic determination, monitoring agent selection, and installation. This segmentation allows each phase to be handled independently and automatically, managing the complexity of comprehensive instrumentation by breaking it down into manageable automated tasks.
3Productivity
If automated entity discovery is performed, then productivity is improved, but measurement precision may worsen due to automatic agent installation
Solution Approach 1:
The system uses feedback from telemetry data to guide the automated discovery and instrumentation process. By analyzing telemetry data from existing instrumented entities, the system discovers uninstrumented entities and determines their characteristics, then selects appropriate monitoring agents based on this feedback. This feedback mechanism ensures that automated instrumentation maintains precision by making informed decisions rather than random selections.
Solution Approach 2:
Telemetry data serves as an intermediary that bridges the gap between automated discovery and precise instrumentation. The system analyzes telemetry data to infer entity characteristics and determine appropriate monitoring agents, using this intermediate information to ensure that automated agent installation maintains measurement precision while improving productivity.
Data Source
AI summary
Information retrieved from monitoring agents currently installed on instrumented entities within a system is analyzed to discover additional entities within the system that are connected to the instrumented entities. Each of these discovered entities is analyzed to determine whether a monitoring agent is able to be installed within the entity; if installation is possible, such installation is automatically performed (or a guided manual installation is implemented utilizing an interface). After a monitoring agent is installed within a discovered entity, information is retrieved from that monitoring agent and is used to discover additional entities within the system that are connected to that discovered entity. In this way, an iterative discovery of all entities within a system may be performed.