Automated Cloud Resource Re-provisioning for Data Analytics
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Solution Overview
Problem
Conventional data analytics solutions face limitations in handling increasing data set sizes and varieties, particularly in cloud computing environments, due to challenges in calculating costs and time consumption, and manual reconfiguration of cloud resources can alter costs, time, and accuracy of analytic results.
Innovation Solution
A method for automatically re-provisioning computing resources by determining differences between initial and revised data analytic plans, computing cost and time parameters, and presenting these to implement selective reconfiguration, thereby improving ease of use and efficiency while incorporating security and privacy modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual reconfiguration of cloud computing resources is performed after initial provisioning, then the computing resources can be adjusted to meet changing data analytic needs, but the cost and time consumption increase significantly and the accuracy of analytic results may be compromised
Solution Approach 1:
The system pre-calculates and stores cost and time parameters for various computing resource configurations before actual reconfiguration is needed. When a data scientist requests resource changes, the system retrieves pre-computed parameters instead of calculating them in real-time, significantly reducing the time consumption while maintaining adaptability.
Solution Approach 2:
An automated intermediary system is introduced between the data scientist and the cloud computing resources. This intermediary automatically determines differences between current and desired configurations, retrieves pre-computed cost and time parameters, and executes reconfiguration without manual intervention, thereby reducing both time consumption and potential errors in analytic results.
2Adaptability or versatility
If manual reconfiguration of cloud computing resources is performed after initial provisioning, then the computing resources can be adjusted to meet changing data analytic needs, but the cost of the data analytics solution becomes difficult to calculate and control
Solution Approach 1:
The system pre-calculates and stores cost parameters for various computing resource configurations before actual reconfiguration is needed. When a data scientist requests resource changes, the system retrieves pre-computed cost parameters instead of calculating them in real-time, significantly reducing the time consumption while maintaining adaptability.
Solution Approach 2:
The system provides automated feedback to data scientists about the cost implications of proposed computing resource reconfigurations. By comparing pre-computed cost parameters of current and desired configurations, the system informs users of cost changes before they commit to reconfiguration, enabling informed decision-making and better cost control.
3Reliability
If automated re-provisioning is implemented to reduce manual intervention, then the accuracy of analytic results is preserved and efficiency is improved, but the complexity of the system increases
Solution Approach 1:
The system performs self-service by automatically determining differences between current and desired computing resource configurations, retrieving pre-computed cost and time parameters, and executing reconfiguration without manual intervention. This automation preserves the accuracy of analytic results by eliminating manual errors while managing complexity through standardized automated processes.
4Adaptability or versatility
If computing resources are reconfigured to handle increasing data set sizes and varieties, then the data analytics solution can process larger and more diverse data, but the cost and time parameters change and may become unmanageable
Solution Approach 1:
The system provides automated feedback to data scientists about the cost and time implications of proposed computing resource reconfigurations. By comparing pre-computed parameters of current and desired configurations, the system informs users of resource changes before they commit to reconfiguration, enabling informed decision-making and better management of complexity.
Solution Approach 2:
An automated intermediary system is introduced between the data scientist and the cloud computing resources. This intermediary automatically determines differences between current and desired configurations, retrieves pre-computed cost and time parameters, and executes reconfiguration without manual intervention, thereby reducing both time consumption and potential errors in analytic results.
Data Source
AI summary
A first work package defining a data analytic plan associated with a given data problem is obtained. The first work package is generated in accordance with an automated data analytics lifecycle and is implemented in a provisioned system. A second work package defining a revised data analytic plan is obtained. A set of differences between the first work package and the second work package is determined. Cost and time parameters, associated with modifying the provisioned system to implement the set of differences between the first work package and the second work package, are computed. The set of differences and the computed cost and time parameters are presented. The provisioned system is automatically re-configured in accordance with at least a portion of the set of differences and based on the computed cost and time parameters.