AI Cloud Subscription Tiering for Automated Service Switching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cloud subscription processes lack automation for customers to efficiently subscribe, monitor, and switch to different tiers based on changing business needs, requiring manual intervention and expert knowledge, which is inefficient and difficult to manage.
Innovation Solution
A language and platform agnostic smart cloud subscription module utilizing machine learning (ML)/artificial intelligence (AI) for automated decision-making to determine, monitor, and switch service tiers based on parameters like traffic data, memory capacity, and CPU usage, allowing seamless integration across different cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual provisioning and monitoring of cloud services is used, then expert knowledge and control are required, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system enables self-service through automated subscription tier selection and dynamic adjustment. The ML model autonomously determines appropriate service tiers based on user inputs and business parameters, eliminating the need for expert intervention. The system continuously monitors usage patterns and automatically adjusts subscriptions, allowing the cloud service management to serve itself without manual provisioning.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Machine learning models and algorithms substitute human expert decision-making, automatically analyzing business parameters, usage patterns, and cost factors to determine optimal subscription tiers. This substitution transforms labor-intensive manual provisioning into automated computational processes.
2Productivity
If automated subscription systems are implemented, then time and effort are reduced, but the system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional platform that handles diverse cloud service subscriptions across different providers and service types. The ML model is designed to work with multiple cloud providers (AWS, Azure, GCP) and various service categories (compute, storage, database), consolidating multiple subscription management functions into a single automated system that adapts to different scenarios.
Solution Approach 2:
The patent introduces an intermediary layer between users and cloud providers - the automated subscription management system with ML models. This intermediary handles the complexity of comparing services, calculating costs, and making subscription decisions, shielding users from underlying system complexity while enabling productive automated management.
3Ease of operation
If customers subscribe without expert knowledge, then accessibility is improved, but subscription accuracy and suitability may deteriorate
Solution Approach 1:
The system implements feedback loops where the ML model continuously monitors actual usage patterns, performance metrics, and cost data against initial subscription selections. This feedback enables the system to learn from real-world performance and automatically adjust or recommend optimal service tiers, ensuring continuous improvement in subscription accuracy while maintaining ease of operation.
Solution Approach 2:
The patent applies preliminary action by having the ML model pre-analyze user inputs, business parameters, and usage patterns before making subscription recommendations. The system performs preliminary assessments of requirements and constraints, preparing optimized subscription configurations in advance, which ensures accurate service tier selection while keeping the user interface simple and accessible.
Data Source
AI summary
Various methods, apparatuses/systems, and media for automated subscription to cloud-based services are disclosed. A processor establishes a communication link between a cloud environment and a user interface via a communication interface, wherein cloud-based services are hosted in the cloud environment. The processor also implements a predefined ML/AI algorithm to determine service tier for each of said cloud-based services; provisions the services based on the determined service tier; continuously monitors preconfigured parameters associated with each service tier; builds a ML/AI based model based on the preconfigured parameters; automatically changes a level of each service tier based on implementing the ML/AI based model; receives user input data to subscribe to the cloud-based services; determines a service tier that matches the received user input data based on the ML/AI based model; and automatically allows subscription to the cloud-based services in accordance with the service tier that matches the received user input data.