Dynamic Cloud Data Filtering via Metric-Driven Rule Adaptation
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Solution Overview
Problem
Modern cloud environments face challenges in managing high data volumes and complex relationships between data sources and consumers, leading to resource overload and inefficiencies due to the inability to dynamically adjust data filtering rules based on changing metrics.
Innovation Solution
A cloud controller dynamically determines filtering rules based on metrics of the cloud computing environment and configures filtering adapters to apply these rules, ensuring data is filtered specifically for each data consumer, thereby reducing resource overload and adapting to changes in infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data filtering rules are applied statically, then device complexity is reduced, but adaptability deteriorates as the system cannot adjust to changing cloud environment metrics
Solution Approach 1:
The patent implements dynamic filtering rules that automatically adjust based on real-time cloud environment metrics such as data volume, velocity, and resource utilization. The system transitions from static to dynamic filtering by continuously monitoring metrics and adapting filtering parameters, enabling the system to respond to changing conditions without manual intervention.
Solution Approach 2:
The system establishes a feedback loop where filtering performance and cloud environment metrics are continuously monitored and fed back to the filtering rule engine. This feedback mechanism enables automatic adjustment of filtering rules based on actual system performance and changing data characteristics, resolving the contradiction between adaptability and complexity.
2Loss of information
If all data is transmitted to data consumers, then information completeness is improved, but network load and resource consumption increase
Solution Approach 1:
The patent applies different filtering rules and data selection criteria tailored to each data consumer's specific needs and characteristics. Instead of uniform filtering, the system customizes data transmission for each consumer based on their requirements, ensuring each receives appropriate data quality while reducing overall network load by eliminating irrelevant data.
Solution Approach 2:
The system implements selective data transmission where only the necessary portion of data is sent to each consumer based on their specific needs. This partial action approach avoids transmitting excessive data that would consume network resources, while still maintaining information completeness for each consumer's specific use case.
3Loss of information
If filtering rules are customized for each data consumer, then data relevance is improved, but device complexity increases due to multiple filtering configurations
Solution Approach 1:
The patent implements a universal filtering rule engine that can dynamically generate and apply consumer-specific filtering rules from a set of general parameters and templates. This multi-functional system handles multiple consumer requirements through a single adaptable mechanism, reducing the complexity of managing separate filtering configurations for each consumer while maintaining high data relevance.
4Productivity
If data transmission velocity is increased, then productivity is improved, but resource overload increases in the cloud environment
Solution Approach 1:
The system performs preliminary filtering and data preprocessing before data transmission, reducing the volume of data that needs to be transmitted at high velocity. By pre-filtering data based on consumer requirements and cloud environment capacity, the system enables faster transmission of relevant data without overloading cloud resources.
Solution Approach 2:
The patent dynamically adjusts transmission parameters such as data velocity, batch size, and compression level based on real-time cloud environment metrics. When resources are constrained, the system automatically reduces transmission velocity or increases compression; when resources are available, it increases velocity to improve productivity, thus balancing both objectives.
Data Source
AI summary
Techniques for dynamically filtering data streams are disclosed. In some embodiments, a computer system performs a method comprising: obtaining one or more metrics of a cloud computing environment, the cloud computing environment including a data source, a data consumer, and a network, the data source configured to transmit data to the data consumer via the network; determining a filtering rule based on the one or more metrics, the filtering rule corresponding to a data filtering operation that modifies data transmissions; and configuring a filter adapter of the data source to apply the data filtering operation of the filtering rule to the data transmissions from the data source to the data consumer.