Container Runtime Profiling for Cross-Language Root Cause Analysis

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Solution Overview

Problem

Current software design methodologies in cloud environments, involving microservices, dynamic scaling, and transitory states, lack efficient mechanisms for performance analysis and root cause identification across multiple programming languages.

Innovation Solution

A computer-implemented method for unified performance profiling that involves searching container runtime interface metadata, selecting applicable profilers, injecting code libraries, and executing profilers across compute nodes to produce results, which are then displayed in a unified graphical interface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If current software design methodologies involving microservices and dynamic scaling are used, then system flexibility and scalability are improved, but performance analysis and root cause identification become more difficult

Engineering Contradiction:

Improvesystem flexibilityVSAvoidperformance analysis difficulty

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system segments the complex cloud environment into discrete compute nodes, containers, and processes. Each component is individually profiled through injected profilers, breaking down the overwhelming complexity of analyzing entire microservice architectures into manageable, isolated units that can be analyzed separately and then correlated.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces profilers as intermediary components that are injected into runtime processes. These profilers act as mediators between the running application and the analysis system, capturing performance data without requiring changes to the application code or infrastructure, thus enabling analysis of dynamic systems without disrupting their operation.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If multiple programming languages are used in the cloud environment, then technology stack versatility is improved, but unified performance profiling becomes more complex

Engineering Contradiction:

Improvetechnology stack versatilityVSAvoidprofiling system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a universal profiler architecture that can profile multiple programming languages (Java, Python, Node.js, Go, etc.) through a single unified system. The profiler selects and injects language-specific profiling libraries based on the runtime process type, allowing one system to handle diverse technology stacks without requiring separate profiling tools for each language.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system changes parameters dynamically based on the detected runtime process type. When a process is identified, the system adjusts which profiler library to inject and how to configure it, adapting the profiling approach to match the specific programming language and runtime environment while maintaining a consistent user interface and analysis framework.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If traditional performance analysis tools are used in dynamic cloud environments, then tool simplicity is maintained, but accuracy of root cause identification deteriorates

Engineering Contradiction:

Improvetool simplicityVSAvoidroot cause identification accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system performs preliminary actions by pre-compiling and preparing language-specific profiler libraries before they are needed. When a runtime process is detected, the appropriate pre-prepared profiler is immediately injected and activated. This preliminary preparation ensures accurate profiling from the start without requiring complex real-time compilation or configuration during the analysis phase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The profiling system implements self-service through automated process detection, profiler selection, and library injection. The system automatically identifies runtime processes, selects the appropriate profiler based on the programming language, injects the necessary code libraries, and configures profiling parameters without manual intervention, maintaining simplicity while achieving high precision through automated adaptive behavior.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12443460B2Performance analysis and root cause identification for cloud computing

Publication Date: 2025.10.14 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Examples described herein provide a computer-implemented method that includes, in response to receiving a request against the workload in an environment comprising predetermined cloud-based containers, searching predetermined container runtime interface metadata across a plurality of compute nodes in the environment to locate runtime processes. The method further includes selecting, for each runtime process located, a respective applicable profiler from a set of predetermined profilers sharing a transactional database. The method further includes injecting, for each runtime process located, predetermined code libraries for each respective applicable profiler. The method further includes re-linking the predetermined code libraries for each respective applicable profiler. The method further includes executing, for each runtime process located, each respective applicable profiler to produce a set of results.