Stream Frame Parsing With Selective Memory Reclamation
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Solution Overview
Problem
Existing data streaming systems face inefficiencies in handling extraneous or outdated data, which are not effectively addressed by existing technologies, particularly in the context of handling extraneous data, such as redundant, outdated, or otherwise surplus information, leading to computational and memory inefficiencies and potential security vulnerabilities.
Innovation Solution
The system creates a new unit of stream data by either slicing or partitioning the original unit to replace partially extraneous data, optimizing computational efficiency or storage efficiency, respectively, by evaluating factors like extraneous data amount, memory resources, and processing capacity, and dynamically adjusting thresholds based on system conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system processes all incoming stream data without filtering, then data completeness is maintained, but computational resources are wasted on extraneous data and memory efficiency deteriorates
Solution Approach 1:
The system extracts and identifies extraneous data portions from incoming stream data units using protocol knowledge and pattern recognition. By separating useful data from extraneous data, the system processes only necessary information, thereby improving computational efficiency while avoiding waste on redundant or malformed data segments.
Solution Approach 2:
The system performs preliminary validation and filtering of stream data before full processing. By checking data units against known protocols and patterns in advance, the system identifies and discards obviously extraneous data early in the pipeline, preventing unnecessary computational resource consumption during subsequent processing stages.
2Quantity of substance
If the system retains all stream data units for processing, then data availability is maximized, but memory consumption increases and reclamation is delayed
Solution Approach 1:
The system implements a selective discarding mechanism that identifies extraneous data units and removes them from memory promptly. By distinguishing between useful and extraneous data using protocol validation and pattern matching, the system recovers memory space associated with discarded data while retaining only necessary data units, thus improving memory efficiency without compromising data availability for valid data.
3Device complexity
If the system uses simple data handling without sophisticated filtering, then system complexity is low, but security vulnerabilities increase due to inability to guard against malicious attacks
Solution Approach 1:
The system employs feedback mechanisms where processing results and error patterns are analyzed to improve extraneous data identification. By monitoring validation failures and attack patterns, the system dynamically adjusts its filtering criteria and protocol knowledge base, enhancing security detection capabilities while maintaining manageable system complexity through adaptive learning.
Solution Approach 2:
The system introduces an intermediary validation layer between raw data reception and full processing. This intermediary layer performs protocol validation, pattern matching, and anomaly detection to filter malicious or extraneous data before it reaches core processing systems, providing security protection without requiring complete system redesign or excessive complexity.
4Ease of operation
If the system processes data without selective replacement, then processing simplicity is maintained, but computational cost increases due to handling redundant data
Solution Approach 1:
The system segments stream data into distinct units and further divides them into useful and extraneous portions using protocol-defined boundaries and validation rules. By processing data in segmented units rather than bulk operations, the system applies computational effort only to valid data segments while efficiently skipping or discarding extraneous portions, reducing overall computational cost while maintaining straightforward processing logic for each segment.
Data Source
AI summary
Techniques for parsing stream data are disclosed. A system receives a first frame of a first set of frames. The first set of frames embeds a second set of frames. The system generates a first runtime object to represent at least part of the first frame that is stored in a first memory section. Based on determining that a first portion of the first frame does not need to be retained in memory, the system evaluates a size of a portion of the first frame relative to a threshold. Based on the evaluation, the system either (a) generates a second runtime object to represent a second portion of the first frame that is copied from the first memory section to a second memory section or (b) generates a third runtime object to represent the second portion of the first frame residing in the first memory section.