AI Scaler for Non-Disposable Cloud Applications
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Solution Overview
Problem
Legacy applications in cloud environments often fail to interface correctly with cloud-based management tools due to non-compliance with twelve-factor application standards, leading to under-provisioning or over-provisioning of resources, which can result in inefficient resource allocation and application downtime.
Innovation Solution
An artificial intelligence (AI) scaler is introduced to dynamically and predictively scale non-disposable applications by monitoring resource utilization, learning patterns from historical data, and adjusting instance numbers to match demand, ensuring efficient resource allocation and minimizing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based management tools are used to automatically adjust resource allocation, then resource allocation efficiency is improved, but legacy applications cannot interface correctly with the tools
Solution Approach 1:
The patent introduces an intermediary layer (wrapper or adapter) between legacy applications and cloud management tools. This intermediary translates legacy application resource requests into the standardized interface format required by cloud management tools, enabling automatic resource allocation for legacy applications without requiring code modification.
Solution Approach 2:
The patent transforms legacy applications into scalable formats by changing their interface parameters and communication protocols. By modifying how legacy applications present their resource requirements (changing parameters like interface format, data structure), they become compatible with cloud management tools while maintaining their core functionality.
2Reliability
If more resources are provisioned to meet peak demand, then application reliability is improved, but resource allocation efficiency deteriorates due to over-provisioning
Solution Approach 1:
The patent implements dynamic resource provisioning where resource allocation automatically adjusts based on real-time application demand. The system continuously monitors application performance metrics and scales resources up or down accordingly, transitioning from static over-provisioning to dynamic adaptive provisioning that maintains reliability while optimizing efficiency.
Solution Approach 2:
The patent establishes a feedback loop where cloud management tools continuously monitor application resource consumption and performance, then automatically adjust resource allocation based on this feedback. This closed-loop system ensures resources are allocated efficiently while maintaining application reliability through predictive scaling.
3Productivity
If resources are reduced to match low demand periods, then resource allocation efficiency is improved, but application downtime increases during scaling operations
Solution Approach 1:
The patent implements predictive scaling that anticipates demand changes before they occur. By analyzing historical patterns and current trends, the system provisions resources in advance of predicted demand spikes, avoiding downtime during scaling operations while maintaining efficient resource allocation during low-demand periods.
Data Source
AI summary
A method includes identifying a cloud application in a cloud environment as a non-disposable application and monitoring a plurality of instances of the non-disposable application running in the cloud environment. The method also includes determining that a number of the instances of the non-disposable application should be modified based on one or more demand predictions by an artificial intelligence scaler, adjusting the number of the instances of the non-disposable application running in the cloud environment based on the one or more demand predictions, and modifying an allocation of one or more resources of the cloud environment associated with adjusting the number of the instances of the non-disposable application.