Cloud Application Configuration via ML Scenario Buckets
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Solution Overview
Problem
Configuring software applications hosted in the cloud to provide an optimal user experience across varying usage scenarios and time frames is challenging due to the need for numerous custom configurations, especially when dealing with seasonal variations and non-normal scenarios such as emergencies, which can lead to inefficiencies and unwieldy configuration management.
Innovation Solution
Implementing a system that uses machine learning models to categorize usage scenarios into 'buckets' and generate standardized configuration parameters, allowing for proactive and reactive adjustments to handle normal and non-normal conditions, thereby simplifying the configuration process and reducing the need for individual customizations for each use case.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If numerous custom configurations are implemented to handle varying usage scenarios and seasonal variations, then the user experience can be optimized for different conditions, but the configuration management becomes unwieldy and time-consuming
Solution Approach 1:
The patent segments the monolithic configuration management into modular configuration modules, where each module handles specific usage scenarios or seasonal variations independently. This allows the system to maintain multiple configurations without creating unwieldy management complexity, as each segment can be managed separately.
Solution Approach 2:
The configuration system is designed with universal configuration modules that can serve multiple usage scenarios and seasonal variations. Rather than creating entirely separate configurations for each scenario, the same configuration infrastructure handles diverse conditions through parameter variations, reducing overall management complexity.
2Adaptability or versatility
If manual configuration adjustments are made to respond to changing conditions, then the application can adapt to current needs, but the administrative time and effort increase significantly
Solution Approach 1:
The system performs preliminary configuration actions by pre-defining configuration modules for anticipated usage scenarios and seasonal variations. When conditions change, the system can switch between pre-configured modules or automatically select appropriate configurations, eliminating the need for manual real-time configuration adjustments.
Solution Approach 2:
The configuration system operates autonomously by automatically selecting and applying appropriate configuration modules based on detected usage scenarios and seasonal patterns. This self-service capability eliminates administrative overhead, as the system adjusts its own configuration without human intervention.
3Device complexity
If standardized configuration parameters are used across different scenarios, then configuration management is simplified, but the ability to optimize for specific usage scenarios is reduced
Solution Approach 1:
The configuration system applies local quality by allowing standardized configuration parameters at the module level while enabling scenario-specific optimizations through parameter customization. Each configuration module maintains a baseline standardized structure, but individual parameters can be adjusted to optimize for specific usage scenarios and seasonal variations.
Data Source
AI summary
Systems and methods may generally be used to configure applications, specifically cloud-based applications or software as a service (SaaS) applications. An example method may include receiving data indicating a performance condition of the network environment. The example method may include classifying the performance condition into one of at least two categories, where the two categories include a normal category indicating a normal condition of the network environment and at least one non-normal category indicating at least one non-normal condition of the network environment. The example method can further include setting configuration parameters corresponding to a selected one of the at least two categories, responsive to detecting at least one performance condition corresponding to the selected category. The application can subsequently be operated in the network based on the configuration parameters.