Adaptive Data Collection Scheduling for Data Center Assets
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Solution Overview
Problem
Current data center management consoles lack control over data collection frequency and prioritization, leading to inefficient use of network bandwidth and resources, particularly as data center assets have varying monitoring needs, with preset intervals being too frequent for some and too infrequent for others, and existing solutions are cumbersome to customize on a large scale.
Innovation Solution
Implementing an adaptive update scheduling operation that adjusts the prioritization and frequency of data center asset data collection, using a system comprising a processor, data bus, and a non-transitory computer-readable storage medium with computer program code to identify and collect data center asset data, and perform adaptive scheduling to provide optimized data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If preset intervals are used for data collection, then data collection is simplified, but monitoring effectiveness deteriorates for assets with varying needs
Solution Approach 1:
The patent implements dynamic data collection intervals based on asset priority levels. Critical assets receive more frequent monitoring updates while non-critical assets receive less frequent updates. The system automatically adjusts collection frequency based on predefined priority criteria, transforming static preset intervals into dynamic, needs-based intervals that optimize both monitoring effectiveness and resource utilization.
2Measurement precision
If frequent data collection is performed, then monitoring accuracy is improved, but network bandwidth and resource usage increase
Solution Approach 1:
The patent applies different data collection frequencies to different assets based on their priority levels. Critical assets receive frequent updates for high monitoring accuracy, while non-critical assets receive less frequent updates to conserve network bandwidth. This localized differentiation ensures that monitoring resources are allocated efficiently according to actual asset importance, improving overall system effectiveness while reducing unnecessary resource consumption.
3Productivity
If custom data collection schedules are implemented, then data collection optimization is improved, but system complexity increases
Solution Approach 1:
The patent segments assets into priority levels (critical, high, medium, low) and applies standardized collection schedules to each segment. This segmentation approach simplifies the scheduling system by using clear categorization criteria rather than complex custom schedules for each asset. The system maintains optimization through automated priority-based routing while keeping the scheduling mechanism itself relatively simple and manageable.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: identifying data center asset data to monitor; collecting data center asset data; and, performing an adaptive update scheduling operation, the adaptive update scheduling operation adaptively adjusting a prioritization and frequency of data center asset data collection to provide adapted data center asset data.