Automated Computing Resource Provisioning via Cost and Time Analysis
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Solution Overview
Problem
Conventional data analytics solutions face limitations in calculating costs and efficiency due to increasing data set sizes and varieties, leading to inefficiencies and challenges in provisioning computing resources.
Innovation Solution
A method that automates the provisioning of computing resources by analyzing data analytics plans and computing resource configurations, considering cost and time parameters, and incorporating security and privacy policies to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual provisioning of computing resources is used, then flexibility in resource configuration is maintained, but cost calculation accuracy and provisioning efficiency deteriorate
Solution Approach 1:
The system automatically computes cost parameters and time parameters for different computing resource configurations without manual intervention. The automated data analytics lifecycle generates work packages that trigger automatic resource provisioning based on computed parameters, eliminating the need for manual cost calculations and resource allocation decisions
Solution Approach 2:
The patent replaces manual mechanical provisioning processes with automated computational systems. Software algorithms automatically analyze work packages, compute cost and time parameters for various resource configurations, and provision resources based on optimized selections, substituting human manual operations with automated computational mechanisms
2Productivity
If computing resources are over-provisioned to handle increasing data sizes, then data analytics capability is improved, but resource utilization efficiency and cost effectiveness deteriorate
Solution Approach 1:
The system dynamically adjusts computing resource provisioning based on the specific requirements of each data analytics work package. By computing cost and time parameters for different configurations and selecting optimal resources for each task, the system adapts resource allocation to actual needs rather than using static over-provisioning, thereby improving both productivity and resource utilization efficiency
Solution Approach 2:
The patent changes the provisioning approach from fixed resource allocation to parameter-based dynamic allocation. By computing and comparing cost parameters and time parameters for different resource configurations, the system selects optimal parameters for each analytics task, enabling efficient resource utilization while maintaining high data analytics capability
3Productivity
If automated resource provisioning is implemented, then provisioning speed and consistency are improved, but system complexity and implementation difficulty worsen
Solution Approach 1:
The system implements a universal automated provisioning framework that handles multiple types of computing resources (processing units, storage, memory) through a single integrated process. The work package analysis and parameter computation mechanism works across different resource types, providing consistent automated provisioning while managing system complexity through standardized multi-functional procedures
Solution Approach 2:
The patent introduces work packages as intermediary objects that carry data analytics requirements from users to the automated provisioning system. These work packages serve as standardized interfaces that simplify the interaction between user needs and complex resource allocation algorithms, enabling fast automated provisioning without exposing system complexity to users
Data Source
AI summary
A work package is obtained defining a data analytic plan for analyzing a given data set associated with a given data problem. The work package is generated in accordance with an automated data analytics lifecycle. The data analytic plan and the given data set are analyzed. Based on at least a portion of results of the analysis, at least one of a cost parameter and a time parameter is computed for one or more computing resource configurations proposed for implementing the data analytic plan. One of the one or more computing resource configurations is selected based on at least one of the cost parameter and the time parameter. A computing resource infrastructure is caused to be provisioned in accordance with the selected computing resource configuration. The above steps are performed on one or more processing elements associated with a computing system.