Server Resource Reallocation Using Processor Utilization Buckets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing server visualization tools either provide high-level summaries that obscure individual server utilization details or overly detailed views that obscure high-level trends, making it difficult to identify underutilized capacity across servers.
Innovation Solution
A system that computes processor utilization rates and generates a graphical user interface (GUI) with a stacked area chart, allowing for visualization of both high-level trends and individual server utilization, while identifying underutilized capacity and recommending resource reallocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If high-level summary visualization is used, then overview of resource utilization is improved, but individual server utilization details are lost
Solution Approach 1:
The visualization is segmented into multiple hierarchical levels: high-level aggregate views showing overall resource utilization trends, and drill-down capabilities that segment the data into individual server details. This allows administrators to view summaries when needed while accessing detailed information when necessary, resolving the contradiction between overview and detail.
Solution Approach 2:
The system adds temporal and hierarchical dimensions to the visualization. By organizing data across multiple time scales (aggregate trends vs. individual server snapshots) and hierarchical levels (farm-level vs. server-level), the system provides both high-level overviews and detailed information without requiring administrators to choose one view over the other.
2Measurement precision
If detailed visualization is used, then individual server utilization is improved, but high-level trends are obscured
Solution Approach 1:
The visualization system segments information display based on the viewer's needs. Detailed server-level metrics are segmented from aggregate farm-level metrics, allowing each to be displayed prominently in context-appropriate views. The system segments the data presentation rather than forcing a single view, preserving both detail accuracy and trend visibility.
Solution Approach 2:
By adding hierarchical and temporal dimensions, the system allows detailed server utilization data and high-level trends to coexist in different layers of the visualization. Administrators can view detailed metrics for specific servers while simultaneously seeing trend indicators at the farm level, effectively adding dimensions rather than choosing between detail and overview.
3Loss of information
If multiple visualization tools are used, then comprehensive information is improved, but complexity of assessment is increased
Solution Approach 1:
The patent merges multiple visualization capabilities into a single unified interface. Instead of requiring administrators to switch between separate tools for different types of resource utilization analysis, the system combines aggregate views, detailed server views, trend analysis, and drill-down capabilities into one integrated visualization tool, reducing complexity while maintaining comprehensiveness.
Solution Approach 2:
The visualization system is designed as a universal tool that performs multiple functions: it provides high-level aggregate views, detailed server-level metrics, temporal trend analysis, and drill-down capabilities all within a single interface. This multi-functional design eliminates the need for multiple separate tools while delivering comprehensive information.
Data Source
AI summary
A system and method for determining and generating a visualization of processor utilization is described. The system accesses a source data set that indicates processor utilization rates of a plurality of servers over a plurality of sampling periods. The system defines a target data set that includes a plurality of processor utilization range buckets corresponding to the plurality of sampling periods. The system updates the target data set based on the source data set. A graphical user interface (GUI) is generated based on the updated target data set and includes a stacked area chart indicating percentages of samples corresponding to the processor utilization range buckets over time. The system distributes, based on the updated target data set, a load of from a first server to a second server based on the processor utilization range bucket of the first server and the processor utilization range bucket of the second server.