Adaptive Data Collection Management in Distributed Systems
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Solution Overview
Problem
Distributed computing systems face challenges in managing massive data from geographically distributed data collection devices, leading to delayed reactions due to limited processor and memory resources, and limited bandwidth, especially when identifying events of interest in real-time.
Innovation Solution
Implementing a gateway and system manager that perform real-time processing and analytics to demarcate events of interest, concentrate data, and adjust data collection parameters based on rules and triggers, optimizing data quality and bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data collection devices continuously transmit all collected data to the central processing center, then the system has complete data for analysis, but the bandwidth consumption increases and processing time delays
Solution Approach 1:
The gateway performs preliminary processing of data before transmission to the central processing center. It pre-filters, aggregates, and validates data locally, so that only processed and relevant data is transmitted. This preliminary action reduces the volume of data needing transmission and processing at the central center, thereby reducing processing delays while maintaining data quality.
Solution Approach 2:
The system segments data processing functions between the gateway (local processing, filtering, aggregation) and the central processing center (comprehensive analysis). This segmentation allows parallel processing of different data subsets, reducing the overall processing time while ensuring that the central center receives only the most relevant data for final analysis.
2Measurement precision
If data collection devices increase measurement quality and transmission frequency, then the system obtains more accurate real-time data, but the bandwidth consumption increases
Solution Approach 1:
The gateway applies local quality filtering and aggregation rules to data before transmission. It maintains high measurement precision by selectively transmitting only the most relevant and accurate data points, while filtering out redundant or low-quality data. This local quality control ensures data accuracy is preserved while significantly reducing bandwidth consumption.
Solution Approach 2:
The system dynamically adjusts data transmission parameters (frequency, resolution, detail level) based on current system conditions, event importance, and bandwidth availability. During normal conditions, transmission frequency is reduced; during events of interest, the system increases transmission quality and frequency. This parameter adaptation maintains data accuracy when needed while conserving bandwidth during normal operation.
3Reliability
If the system processes and analyzes all incoming data in real-time, then the system identifies events of interest accurately, but the processor and memory resources are overwhelmed
Solution Approach 1:
The gateway performs preliminary filtering, aggregation, and validation of data before it reaches the central processing center. It pre-identifies potential events of interest using local rules and thresholds, so that the central center only needs to process pre-screened data. This preliminary action maintains event detection accuracy while significantly reducing the processing resource requirements at the central center.
Solution Approach 2:
The system extracts and transmits only the most relevant features and events from the raw data stream, rather than processing all raw data in full. The gateway extracts key parameters, anomalies, and event markers, removing redundant information before transmission. This extraction approach maintains event detection accuracy while reducing the complexity and resource requirements of central processing.
4Loss of information
If the system transmits all raw data from data collection devices, then the system has complete information for analysis, but the bandwidth is insufficient to handle the data volume
Solution Approach 1:
The gateway merges and aggregates data from multiple sources and time points before transmission to the central processing center. It combines redundant measurements, aggregates statistical summaries, and consolidates related data streams. This merging process reduces the total volume of data transmitted while preserving the essential information content needed for comprehensive analysis.
Solution Approach 2:
The gateway performs preliminary data consolidation, filtering, and aggregation locally before transmission. It pre-processes data to eliminate duplicates, fill gaps using local algorithms, and aggregate related measurements. This preliminary action ensures information completeness is maintained while significantly reducing the bandwidth required for data transmission to the central center.
Data Source
AI summary
Methods and apparatus to adaptively manage data collection devices in distributed computing systems are disclosed. Example disclosed methods involve instructing a first data collection device to operate according to a first rule. The example first rule specifies a first operating mode and defining a first event of interest. Example disclosed methods also involve obtaining first data from the first data collection device while operating according to the first rule. Example disclosed methods also involve, in response to determining that the first event of interest has occurred based on the first data, providing a second rule based on the first data to the first data collection device, and providing a third rule to a second data collection device. The example second rule specifies a second operating mode and defines a second event of interest, and the examples third rule specifies a third operating mode.