Cloud Data Analysis VM Storage Integration
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Solution Overview
Problem
Current big data analysis in cloud environments requires transmitting data from physical storage to analysis servers, leading to prolonged analysis times and increased network traffic, as well as the need for complex distributed computing systems and additional virtual machine generation for scaling.
Innovation Solution
A system and method that utilize cloud resources to provide a data analysis service by generating clustered virtual machines with storage capabilities, where data is analyzed within the virtual machine environment without the need for file movement, allowing for rapid analysis and resource scaling through CPU and memory allocation within existing virtual machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transmitted from physical storage to analysis server for big data analysis, then data analysis can be performed, but analysis time increases and network traffic increases
Solution Approach 1:
Instead of moving data from storage to analysis server (traditional approach), the patent inverts the approach by bringing the analysis environment to the data location. Virtual machines are deployed directly on the storage system, allowing data analysis to be performed where the data resides, thereby eliminating transmission time and network traffic overhead.
Solution Approach 2:
The patent introduces a virtual machine as an intermediary layer between physical storage and analysis operations. The virtual machine acts as a mediator that provides computing resources directly at the storage location, enabling data analysis without requiring data movement while maintaining abstraction and manageability.
2Productivity
If additional virtual machines are generated for scaling analysis resources, then analysis capacity increases, but system complexity and resource overhead increase
Solution Approach 1:
The patent merges the storage function and computing function into a single integrated system. By deploying virtual machines directly on the storage infrastructure, it combines data storage and data processing capabilities in one platform, reducing the need for separate analysis servers and simplifying system architecture while maintaining scalability.
Solution Approach 2:
The storage system is designed to perform multiple functions: it serves as both data storage infrastructure and computing platform. The virtual machines deployed on storage can handle various data analysis workloads, making the system universal and multi-functional, thereby reducing overall system complexity compared to dedicated separate systems.
Data Source
AI summary
The present invention relates to a system and a method for providing a data analysis service in a cloud environment which does not need to transmit data to an analysis section from a file storage section when providing an analysis service about big data. According to the present invention, it is not needed to specifically move files when analyzing big data, by using the storage section resource in a virtual machine in a cloud environment as storages, and accordingly, the analysis time can be considerably reduced.