Virtual Server Provisioning via Service Scale Parameter Lookup
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Solution Overview
Problem
Current information processing systems lack an efficient method to determine and implement the optimal charge plan and number of virtual servers for cloud services based on user service requirements, leading to potential overprovisioning or underprovisioning of resources.
Innovation Solution
An information processing system that calculates and displays the optimal charge plan and number of virtual servers by referencing a table associating performance and charge plans, allowing users to input service scale parameters and automatically building the necessary virtual servers through a network-connected environment building apparatus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual calculation and determination of virtual server requirements is performed, then flexibility and customization are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system enables self-service by automatically determining the number of virtual servers and charge plans based on user input service scale parameters. The determination unit autonomously queries the table to obtain optimal configurations without requiring manual calculation or expert intervention, allowing users to independently complete resource planning.
Solution Approach 2:
The table is pre-populated with performance data and charge plan information for various service scales. This preliminary preparation allows the determination unit to quickly retrieve optimal configurations through simple parameter matching, eliminating the need for real-time complex calculations when users need resource recommendations.
2Adaptability or versatility
If manual calculation and determination of virtual server requirements is performed, then flexibility and customization are improved, but operational complexity and difficulty increase
Solution Approach 1:
The system performs automatic determination of virtual server requirements through self-service mechanisms. The determination unit automatically queries the pre-prepared table using service scale parameters as keys, retrieving optimal configurations without requiring users to understand complex calculation methodologies or perform manual analysis.
Solution Approach 2:
The table serves as an intermediary between user service requirements and optimal virtual server configurations. It pre-stores the complex relationships between service scales and resource allocations, allowing the determination unit to translate user inputs into recommended configurations through simple table lookups rather than complex real-time computations.
3Reliability
If overprovisioning of virtual servers is performed, then service reliability and performance are improved, but resource waste and cost increase
Solution Approach 1:
The system uses parameter changes to match virtual server configurations to actual service needs. By accepting service scale parameters (such as number of users, data volume, transaction frequency) as input, the determination unit queries the table to obtain corresponding optimal virtual server specifications, ensuring resource allocation closely matches actual demand rather than using fixed overprovisioned configurations.
Solution Approach 2:
The system establishes a feedback mechanism where service scale parameters reflect actual user需求的 feedback. The determination unit uses this feedback to query the table and obtain recommended configurations that adapt to real service requirements, preventing both overprovisioning and underprovisioning by continuously aligning resource allocation with actual usage patterns.
4Loss of energy
If underprovisioning of virtual servers is performed, then cost reduction is improved, but service performance and user satisfaction deteriorate
Solution Approach 1:
The system dynamically adjusts virtual server configuration parameters based on service scale inputs. Rather than using fixed underprovisioned configurations to reduce costs, the determination unit queries the table with actual service parameters to obtain optimal resource allocations that ensure sufficient performance while minimizing waste, adapting resource levels to match actual service demands.
Solution Approach 2:
The system uses service scale parameters as feedback to determine appropriate resource allocation. By continuously monitoring and responding to actual service requirements through parameter-based table queries, the system ensures that virtual server provisioning is sufficient to maintain service performance while optimizing cost efficiency, preventing underprovisioning that would degrade user experience.
Data Source
AI summary
An information processing system, apparatus and method are provided. The information processing system receives input of a service scale of a user service from a terminal device, determines a charge plan, performance, and optimum number of virtual servers for providing the user service with a usage amount and number of devices included in the service scale that is received, by referring to a table in which the performance and the charge plan of the virtual server are associated with each other, and displays information including the charge plan and the number of virtual servers that are determined.