Cross-Environment Application Performance Correlation
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Solution Overview
Problem
In cloud computing environments, customers face difficulties in debugging and troubleshooting applications that span multiple logically isolated network environments, as diagnostic information and performance metrics cannot cross boundaries, leading to costly and time-consuming issue resolution.
Innovation Solution
The application advisor tools provide performance insights and recommendations across multiple logically isolated network environments by correlating events and determining actions to improve application performance, using techniques such as machine learning to infer correlations and remediation across virtual and physical resource environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network environments are logically isolated for security and privacy, then security and privacy are improved, but diagnostic information cannot cross boundaries leading to increased troubleshooting complexity
Solution Approach 1:
The patent introduces an intermediary component that sits at the boundary of isolated network environments, allowing diagnostic information to be shared across boundaries without compromising security. This intermediary enables controlled information exchange while maintaining the logical isolation that protects security and privacy.
Solution Approach 2:
The patent segments diagnostic information into different levels of detail and sensitivity, allowing only appropriate portions to cross network boundaries. This segmentation enables security-conscious information sharing where sensitive data remains isolated while non-sensitive diagnostic data can be analyzed across environments.
2Reliability
If diagnostic information is restricted across isolated networks, then security boundaries are maintained, but mean time to resolution increases
Solution Approach 1:
The patent implements preliminary actions by pre-configuring authorized information sharing pathways and pre-processing diagnostic data for cross-boundary analysis. This allows troubleshooting to proceed faster across isolated networks without compromising security, as the framework is already in place to handle inter-environment communication securely.
Solution Approach 2:
The patent establishes feedback mechanisms where diagnostic information flows from isolated environments to analysis systems, and remediation recommendations flow back. This closed-loop feedback enables rapid iteration and resolution while maintaining security boundaries, as each environment only exposes necessary information and receives actionable insights without exposing sensitive data.
3Adaptability or versatility
If multiple isolated network environments are used for cloud services, then service versatility is improved, but application performance debugging becomes more difficult
Solution Approach 1:
The patent creates a universal troubleshooting framework that can operate across multiple isolated network environments simultaneously. This multi-functional system can analyze applications spanning different cloud providers and network boundaries using a single unified approach, reducing debugging difficulty while preserving the versatility of having multiple isolated environments.
Solution Approach 2:
The patent introduces intermediary analysis systems that act as neutral mediators between isolated network environments. These intermediaries can correlate events and performance metrics across boundaries without requiring direct access to sensitive internal systems, enabling effective debugging of multi-environment applications while maintaining the security and versatility benefits of isolation.
Data Source
AI summary
Applications may execute using resources from multiple environments or ecosystems, such as may include virtual resources from a virtual resource environment hosted on physical resources of a cloud provider environment. An event manager in the cloud provider environment can obtain virtualization performance data from the virtual resource environment, as well as performance data from within the cloud provider environment. This data can be fed to an inference engine that can correlate information from the separate environments, and these correlations can be used to generate recommendations for performance adjustments in either the physical or virtual resource environment.