Policy-Based Hybrid Cloud Scaling Across Providers
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Solution Overview
Problem
Current hybrid cloud solutions lack the ability to dynamically scale resources across multiple cloud providers, leading to either over-provisioning and wasteful resource allocation or under-provisioning, which can result in performance issues.
Innovation Solution
A policy-based system that evaluates scaling requests against policies of multiple cloud service providers, allowing for automatic or approved resource allocation across different cloud environments to meet workload demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are initially provisioned with an increased amount to ensure performance in hybrid cloud environments, then performance reliability is improved, but resource waste and cost increase
Solution Approach 1:
The patent implements dynamic resource provisioning that automatically adjusts resource allocation based on real-time workload demands across hybrid cloud environments. The system continuously monitors resource usage and dynamically scales resources up or down, transitioning from static over-provisioning to adaptive resource management that maintains performance reliability while eliminating resource waste.
Solution Approach 2:
The patent employs feedback mechanisms where the system monitors actual resource utilization and workload demands, then uses this information to adjust resource allocation decisions. The feedback loop enables the system to learn from past resource usage patterns and optimize future provisioning decisions, ensuring performance requirements are met while minimizing resource waste through data-driven allocation.
2Loss of energy
If resources are initially provisioned with a decreased amount to reduce cost in hybrid cloud environments, then cost efficiency is improved, but performance issues arise
Solution Approach 1:
The system enables dynamic resource allocation that automatically scales resources up when workload demands increase and scales down when demands decrease. This dynamic approach allows the system to maintain cost efficiency by avoiding permanent over-provisioning while ensuring performance reliability is maintained during peak demand periods through automated resource acquisition and allocation.
Solution Approach 2:
The patent implements preliminary resource preparation and pre-positioning mechanisms that anticipate future workload demands. By predicting resource needs based on historical patterns and current trends, the system can pre-allocate or pre-provision resources before peak demands occur, ensuring performance reliability is maintained while avoiding the need for permanent over-provisioning that would increase costs.
3Productivity
If automatic resource scaling is enabled across multiple cloud providers, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary resource management layer that sits between multiple cloud providers and the workloads. This intermediary layer abstracts the complexity of multi-cloud resource management by providing a unified interface and centralized control plane. It handles the complexity of coordinating resources across different cloud providers, managing authentication, authorization, and resource orchestration, thereby enabling efficient automatic scaling without exposing system complexity to end users or application developers.
Data Source
AI summary
An approach is provided in which an information handling system receives a scaling request corresponding to an application that includes multiple workloads executing on a first cloud environment and a second cloud environment. The first cloud environment is managed by a first service provider and the second cloud environment is managed by a second service provider. The information handling system evaluates the scaling request against a first set of policies corresponding to the first service provider and against a second set of policies corresponding to the second service provider. In turn, the information handling system scales, in response to the evaluating, one or more first resources on the first cloud environment and one or more second resources on the second cloud environment to fulfill the scaling request.