Server Resource Allocation via Usage-Based User Categorization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for allocating server resources are inefficient due to reliance on assumptions rather than actual usage data, leading to inadequate resource allocation and potential financial losses, as businesses struggle to categorize users based on their resource consumption and prioritize needs effectively.
Innovation Solution
A user categorization system and method that utilizes actual server data to categorize users by their resource consumption and assigns relative business value to each group, employing a double level of abstraction through design patterns and idiomatic cross-references to facilitate resource allocation, connecting programming styles to business needs and using data mining techniques to extract meaningful patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If server resources are allocated according to established priorities, then resource allocation can be managed, but it is difficult to establish accurate priorities and the allocation may not meet actual user needs
Solution Approach 1:
The system implements feedback by continuously monitoring actual server resource usage data and using it to refine user categorization and priority establishment. Usage data is collected, analyzed, and fed back into the categorization system to improve future resource allocation decisions, creating a closed-loop system that adapts to actual user behavior patterns.
Solution Approach 2:
The system enables self-service by automatically categorizing users and allocating resources based on monitored usage patterns without requiring manual intervention. The categorization system autonomously processes usage data, identifies user groups, and determines resource allocation strategies, reducing the need for manual priority establishment while improving accuracy.
2Loss of information
If user-reported usage data is collected through surveys, then usage information can be gathered, but users may over- or under-estimate their usage making the data inaccurate
Solution Approach 1:
The system replaces the mechanical survey method with an automated electronic monitoring system that directly collects usage data from server logs and usage records. This substitution eliminates human estimation errors by using objective, machine-collected data from actual server interactions, thereby improving both completeness and accuracy of usage information.
3Ease of operation
If resource allocation considers the needs of each user group, then user needs may be satisfied, but the business or organization's needs may be overlooked
Solution Approach 1:
The system applies local quality by assigning different weights and priorities to different user groups based on their specific characteristics and business value. Instead of uniform treatment, the categorization system identifies distinct user segments and applies tailored resource allocation strategies to each group, allowing simultaneous optimization for both user satisfaction and business objectives through differentiated quality settings.
Data Source
AI summary
An organization categorization system and method is disclosed. The organization categorization system and method relies on server data to discover which business organizations are consuming the finite resources of the server and in what proportions. Organizations are categorized according to their consumption of resources. The categorization system and method further ascribes a relative business value to each organization to facilitate the allocation of resources among the various organizations in a business. In an example embodiment, users of the server resources use the SAS programming language and the server resources execute SAS applications that support the SAS programming language. The organization categorization system and method connects an executed computer program to a business-defined classification of applicability to purpose. The system and method employs a double level of abstraction to link specific programming styles, first to a general solution case (“design pattern”), and then to link the general solution idiomatically to the business case.