Dynamic Rule Sequencing for Data Categorization Latency
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Solution Overview
Problem
In cloud-based environments, resource metering processes often introduce latency and network jitter due to inefficient rule sequencing in data processing, which can significantly impact throughput and user experience, especially in systems handling large volumes of data.
Innovation Solution
A system dynamically optimizes the ordering of rules based on matching frequency, velocity, and acceleration to minimize processing time, by periodically inspecting rule matching counts and modifying the sequence to prioritize frequently matched rules, either by rearranging existing rules or creating a new set with an optimized order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If resource metering processes are implemented to track data usage, then service providers can determine appropriate charges for customers, but latency and network jitter are introduced due to inefficient rule sequencing
Solution Approach 1:
The patent implements dynamic rule sequencing where the order of rules is not fixed but adapts based on runtime statistics. The system periodically collects matching counts for each rule and reorders them dynamically, transforming a static rule processing system into a dynamic one that optimizes performance while maintaining accurate metering measurements.
Solution Approach 2:
The system changes the parameter of rule ordering based on matching frequency statistics. By monitoring how often each rule matches and adjusting the sequence accordingly, the system optimizes processing efficiency without compromising the accuracy of resource usage tracking.
2Productivity
If rules are reordered to prioritize frequently matched rules, then processing throughput is improved and latency is reduced, but system complexity increases due to dynamic optimization requirements
Solution Approach 1:
The system implements feedback mechanisms by periodically inspecting rule matching counts and using this information to reorder rules. This feedback loop enables the system to automatically optimize its own performance without external intervention, improving throughput while managing complexity through self-adjustment.
Solution Approach 2:
The rule sequencing system performs self-optimization by automatically monitoring its own performance metrics and reordering rules based on observed patterns. This self-service approach eliminates the need for manual rule management while improving processing efficiency.
3Speed
If dynamic rule reordering is implemented to optimize processing speed, then latency and network jitter are minimized, but disruptions to regular processing occur during rule changes
Solution Approach 1:
The system performs rule reordering periodically rather than continuously or on every data packet. This periodic action allows the system to maintain stable processing during rule changes while still capturing optimization benefits, reducing disruptions to regular processing operations.
Solution Approach 2:
The system collects matching statistics in advance before performing rule reordering. By gathering data on rule matching frequencies beforehand and then applying the reordering based on this pre-collected information, the system minimizes processing disruptions while achieving speed optimization.
Data Source
AI summary
A set of techniques is described for optimizing the categorization of data items in a computing system. The techniques include continuously metering data items by traversing each data item through a chain of rules in a sequential order until the data item matches a rule. Once the item matches the rule, it can be successfully categorized. The system can then analyze the number of matches for each rule over a period of time and optimize the sequential order of the chain of rules according to the analysis of the number of matches. For example, the system can modify the sequential order by arranging the rules according to the number of matches of each rule. Alternatively, the system may compute a velocity of matches and use it to optimize the sequential order. Alternatively, the system may use the rate of change to optimize the sequential order.