Multi-Cloud Auto-Scaling Manager for Elastic Resource Allocation
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Solution Overview
Problem
Existing auto-scaling approaches in cloud computing are limited to single cloud environments, restricting elasticity and failing to consider multiple user requirements, such as cost, security, and load balancing, thereby limiting users' options for infrastructure design and scalability.
Innovation Solution
A method and system that automatically scale user compute instances across multiple cloud providers, considering user preferences, cloud properties, and constraints, to determine when and where to scale, and which resources to add or remove, enabling flexible auto-scaling management across multiple cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing auto-scaling approaches are used, then scaling can be accomplished within a single cloud environment, but elasticity and user options are limited
Solution Approach 1:
The patent implements a multi-cloud auto-scaling system that can operate across multiple cloud providers (AWS, Azure, Google Cloud, etc.), making the system universal and adaptable to different cloud environments. The scaling manager evaluates multiple clouds and can scale instances across them, providing versatility in cloud environment options while maintaining a unified control plane.
Solution Approach 2:
The patent introduces a scaling manager as an intermediary component that sits between the user's application and multiple cloud providers. This mediator handles the complexity of multi-cloud management by abstracting cloud-specific details and providing a unified interface for auto-scaling operations across different cloud environments.
2Productivity
If single cloud auto-scaling is implemented, then implementation is simpler, but cost optimization and load balancing across providers cannot be achieved
Solution Approach 1:
The patent implements feedback mechanisms where the scaling manager continuously monitors cloud properties (cost, performance, availability) and user preferences, then uses this feedback to make intelligent scaling decisions. The system evaluates multiple clouds based on real-time conditions and adjusts scaling actions to optimize resource utilization, cost efficiency, and load distribution across providers.
Solution Approach 2:
The patent creates a dynamic multi-cloud auto-scaling system that can adaptively shift workloads between different cloud providers based on real-time conditions such as cost fluctuations, performance metrics, and availability. The system dynamically selects which cloud to scale to or from, enabling flexible resource optimization across multiple providers rather than static single-cloud deployment.
3Extent of automation
If manual scaling decisions are made, then control over scaling actions is maintained, but responsiveness to changing conditions is slower
Solution Approach 1:
The patent implements self-service auto-scaling where the scaling manager autonomously monitors system conditions, evaluates cloud properties, and executes scaling decisions without requiring manual user intervention. The system automatically scales instances to or from different cloud providers based on real-time conditions and user-defined policies, providing rapid responsiveness to changing workload demands while maintaining user-defined preferences and constraints.
Data Source
AI summary
Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.