Hybrid Cloud Resource Management via Automated VM Scaling
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Solution Overview
Problem
Current resource expansion technologies in hybrid cloud systems are unable to meet the dynamic needs of network services, leading to inefficient use of public cloud resources and increased costs, as administrators struggle to manually manage and optimize virtual machine resources across different cloud environments.
Innovation Solution
A management system and method that utilizes a resource monitor, analysis and determination device, and resource deployment device to automatically collect performance data, trigger resource deployments, and adjust virtual machine resources between first and second cloud systems based on predefined conditions, enabling dynamic scaling and recycling of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual resource management is used in hybrid cloud systems, then administrators can control resource deployment, but the system cannot respond dynamically to changing resource demands and leads to inefficient resource utilization
Solution Approach 1:
The system enables self-service resource management where the resource management device automatically monitors performance data, determines deployment needs, and executes resource allocation without requiring continuous manual intervention from administrators, thereby improving productivity while reducing manual work
Solution Approach 2:
The system implements a feedback mechanism where performance monitoring data from virtual machines is continuously collected and analyzed to automatically trigger resource deployment decisions, creating a closed-loop system that dynamically responds to changing resource demands
2Quantity of substance
If public cloud resources are used to expand capacity, then hardware server costs are reduced, but resource utilization is inefficient and costs increase due to inability to automatically measure and optimize usage statuses
Solution Approach 1:
The system uses feedback from performance monitoring data to automatically adjust resource deployment between private and public clouds, optimizing resource utilization by deploying resources only when and where needed, thereby preventing waste while maintaining sufficient capacity
Solution Approach 2:
The system dynamically adjusts resource allocation strategies based on real-time performance data and usage patterns, enabling flexible scaling between private and public cloud resources to match actual demand and avoid fixed infrastructure costs
3Adaptability or versatility
If resource auto-scaling technology is implemented, then service capacity can be expanded, but the system cannot meet diverse network service needs and requires manual decision-making for resource deployment
Solution Approach 1:
The resource management device performs self-service by automatically analyzing performance data, determining appropriate deployment targets and types, and executing resource allocation without requiring administrators to manually configure complex scaling policies for different service types
Solution Approach 2:
The system provides universal resource management capabilities that can handle multiple service types and deployment scenarios through a single integrated platform, eliminating the need for separate manual management processes for each service category
Data Source
AI summary
A management method of cloud resources is provided for use in a hybrid cloud system with first and second cloud systems, wherein the first cloud system includes first servers operating first virtual machines (VMs) and the second cloud system includes second servers operating second VMs, the method including the step of: collecting, by a resource monitor, performance monitoring data of the first VMs within the first servers; analyzing, by an analysis and determination device, the performance monitoring data collected to automatically send a trigger signal in response to determining that a predetermined trigger condition is met, wherein the trigger signal indicates a deployment target and a deployment type; and automatically performing, by a resource deployment device, an operation corresponding to the deployment type on the deployment target in the second cloud system in response to the trigger signal.