Cloud Platform Virtual Machine Script Execution for Data Visualization
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Solution Overview
Problem
Current computing systems face challenges in efficiently processing and visualizing large datasets in cloud environments, particularly for natural language processing, due to complexities in parallelization and deployment on cloud platforms.
Innovation Solution
A cloud-based platform that manages virtual machines to execute scripts for data visualization, involving data retrieval, caching, compression, and execution on available virtual machines, with the option to use external runtime instances, enabling efficient generation and delivery of visualizations to client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is processed and visualized in cloud environments using traditional methods, then visualization capability is provided, but processing efficiency and resource utilization are insufficient for large datasets
Solution Approach 1:
The patent introduces a cloud-based platform as an intermediary between client devices and computational resources. This platform manages virtual machines, handles script execution, and coordinates data processing tasks, thereby resolving the complexity of direct parallelization and deployment while improving processing efficiency through centralized resource management
Solution Approach 2:
The system segments data processing into discrete script execution tasks that can be distributed across multiple virtual machines. By breaking down large-scale data processing into smaller, manageable script-based operations, the system achieves efficient parallelization without requiring complex direct coordination between processing units
2Use of energy by moving object
If virtual machines are used to execute scripts for data visualization, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The cloud-based platform implements self-service mechanisms where virtual machines automatically execute scripts, manage their own runtime environments, and handle data processing tasks without requiring manual intervention. This automation improves resource utilization while masking the underlying complexity of virtual machine management from end users
Solution Approach 2:
The virtual machines are designed as universal computing units capable of executing various types of data processing scripts and supporting multiple programming languages. This multi-functionality allows a single VM infrastructure to handle diverse analytics tasks, improving resource utilization without proportionally increasing management complexity
3Adaptability or versatility
If external runtime instances are used for script execution, then flexibility and adaptability are improved, but connection and coordination complexity increases
Solution Approach 1:
The cloud-based platform serves as an intermediary layer between external runtime instances and the data processing system. It manages connections to external instances, coordinates task distribution, and handles data flow, thereby providing flexibility through external runtime support while masking the complexity of connection management from users
Data Source
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AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives from a client device a request to execute a set of scripts on a set of data in a data model. The program further identifies an available virtual machine (VM) in a plurality of VMs. The program also loads a runtime instance configured to execute the set of scripts onto the identified VM. The program further instructs the runtime instance to execute the set of scripts in order to generate a visualization of the set of data. The program also sends the generated visualization of the set of data to the client device.