Automated Server Virtualization Resource Estimation
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Solution Overview
Problem
The current server sizing process for IT infrastructure is manual, time-consuming, and lacks standard practices, leading to inaccurate results and high resource requirements, making it challenging for IT vendors and customers to agree on optimal resource allocation for server virtualization.
Innovation Solution
A method and system for estimating optimal resources for server virtualization that involves receiving input data, filtering it using predefined rules, generating current IT landscape information, determining virtualization parameters, and providing recommendations for optimal resource allocation through an estimation computing device with a processor and memory, which automates the process and utilizes self-learning techniques for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual server sizing process is used with multiple IT personnel working in closed room environment for 2-3 weeks, then comprehensive discussion and debate can be conducted, but the process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of multiple personnel discussing and debating server sizing requirements with an automated computer-based system. The estimation computing device automatically processes input data, applies virtualization parameters, and generates server sizing recommendations without requiring human intervention in the calculation process, thereby dramatically reducing time while maintaining accuracy through standardized algorithms
Solution Approach 2:
The system enables self-service by allowing the estimation computing device to autonomously perform server sizing calculations. The device receives input data, automatically determines virtualization parameters, and generates output without requiring continuous human oversight or manual computation, making the process efficient and repeatable
2Reliability
If manual server sizing process is used with multiple IT personnel, then various perspectives can be considered, but the process requires a large amount of human resources and is costly
Solution Approach 1:
The patent substitutes human analysts with an automated estimation computing device that processes server sizing requirements. The system maintains analytical completeness by systematically evaluating all input data against predefined virtualization parameters and industry standards, eliminating the need for multiple personnel while ensuring thorough analysis through automated rule-based processing
3Productivity
If automated estimation computing device is used for server virtualization sizing, then the process becomes fast and resource-efficient, but standard practices and principles must be predefined
Solution Approach 1:
The patent applies preliminary action by pre-defining virtualization parameters, filtering rules, and estimation algorithms before the server sizing process begins. These standardized practices are configured in advance in the estimation computing device, allowing rapid automated processing of server sizing requirements without requiring complex real-time decision-making, thus achieving both speed and reliability
Data Source
AI summary
Embodiments of the present disclosure disclose a method and a device for estimating optimal resources for server virtualization. The method comprises receiving input data relating to requirements of server virtualization from a user device. The method further comprises filtering the input data by applying filtering rules. The method further comprises generating current landscape information of a plurality of servers using the filtered input data. The method further comprises determining values of virtualization parameters for a plurality of target servers using the current landscape information and predefined rules. The method further comprises determining landscape information of the plurality of target servers using the current landscape information and the values of virtualization parameters for estimating optimal resources for server virtualization.


