Cloud VM Selection via Historical Load Analysis
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Solution Overview
Problem
Deploying data protection microservices on cloud platforms faces challenges such as high costs, inefficient resource utilization, and lack of global resource optimization across multiple cloud providers, leading to manual and costly virtual machine management.
Innovation Solution
A method that determines a target time period and computing resource needs based on historical data, calculates interruption tolerance, and selects the most cost-effective virtual machine type across multiple cloud platforms to automatically extend and optimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If microservices are deployed on cloud platforms to reduce costs, then resource utilization improves, but manual virtual machine management increases operational complexity and costs
Solution Approach 1:
The system automatically selects and manages virtual machine types based on historical data and current computing needs, eliminating manual intervention. The method autonomously determines target time periods, computing resources, and virtual machine configurations, allowing the system to serve itself rather than requiring manual management of virtual machines across cloud platforms.
Solution Approach 2:
The system dynamically adjusts virtual machine parameters such as CPU cores, memory size, and storage capacity based on analyzed historical data and current application requirements. By changing these parameters automatically according to actual needs, the system optimizes resource utilization while reducing the complexity of manual configuration and management.
2Adaptability or versatility
If virtual machine types are manually selected for each cloud platform, then deployment flexibility is maintained, but time consumption and operational costs increase
Solution Approach 1:
The system pre-analyzes historical data to identify patterns in computing resource usage and determines optimal virtual machine configurations in advance. By performing this analysis beforehand, the system has virtual machine selection recommendations ready when deployment is needed, significantly reducing the time required for manual selection while maintaining flexibility through data-driven adaptability.
Solution Approach 2:
The system continuously monitors and analyzes historical running data of applications, using this feedback to automatically determine optimal virtual machine types for different cloud platforms. This feedback loop enables the system to adapt to changing requirements over time, maintaining deployment flexibility while eliminating manual selection processes and associated time losses.
3Reliability
If computing resources are increased to handle high load periods, then application performance is maintained, but resource costs increase during low utilization periods
Solution Approach 1:
The system dynamically adjusts virtual machine资源配置 based on analyzed historical data and predicted load patterns. Instead of static over-provisioning, the method enables flexible scaling of computing resources that adapts to actual demand, maintaining application performance during high-load periods while reducing resource allocation and costs during low-utilization periods.
Solution Approach 2:
The system uses historical data analysis to predict future computing resource needs and prepares appropriate virtual machine configurations in advance. This preliminary action allows the system to proactively allocate resources before peak demand occurs, ensuring performance reliability while avoiding unnecessary resource allocation during low-demand periods, thus optimizing cost efficiency.
Data Source
AI summary
Embodiments of the present disclosure relate to a method for running an application, an electronic device, and a computer program product. The method includes determining, based on historical data associated with running of the application, a target time period and a computing resource to be used for running the application within the target time period, a load rate associated with the computing resource being higher than a threshold load rate in the target time period. The method further includes determining an interruption tolerance of the application based on a type of the application, determining costs for running the application by a plurality of types of virtual machines and determining a target type from the plurality of types based on the costs and the computing resource, to cause the application to be run by a virtual machine of the target type.