Dynamic Adaptive Machine Packet Processing Load Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing packet processing systems in digital communications networks face challenges in managing varying processor loads and packet streams, leading to excessive communication delays and dropped packets due to unpredictable changes in packet volume, size, and processor availability.
Innovation Solution
A dynamically adaptive system that designates processing levels to packet groups based on load measures, using a controller to configure processing engines and adjust operational levels in response to changing conditions, minimizing excessive processing and false-positive signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple processing engines are applied to packets to improve processing thoroughness and security, then processing reliability is improved, but processor load increases causing communication delays and packet drops
Solution Approach 1:
The system dynamically adjusts the number of processing engines applied to packet groups based on real-time processor load conditions. The controller monitors processor availability and adapts the processing level from full (multiple engines) to partial (fewer engines) processing, resolving the contradiction between maintaining high reliability and avoiding excessive delays under varying load conditions.
Solution Approach 2:
The system changes the processing parameter (number of processing engines) based on processor load measurements. When load is low, more processing engines are applied to improve reliability; when load is high, fewer engines are applied to reduce delay, thus adapting the processing intensity to current system conditions.
2Reliability
If full processing level is applied to all packet groups, then processing thoroughness is improved, but processor load becomes excessive causing packet drops
Solution Approach 1:
The system applies different processing qualities to different packet groups based on local conditions. Some packet groups receive full processing (high thoroughness) when processors are available, while others receive partial processing (reduced thoroughness) when load is high. This local differentiation maintains overall system productivity while preserving processing quality where possible.
Solution Approach 2:
The system implements partial processing for certain packet groups when full processing would exceed processor capacity. By applying only necessary or reduced processing to some packets, the system maintains overall throughput and avoids packet drops, accepting that processing thoroughness will be reduced for those specific groups.
3Loss of time
If processing level is reduced to minimize delays, then communication delay is reduced, but processing reliability deteriorates
Solution Approach 1:
The system dynamically adjusts processing level based on real-time conditions rather than using a fixed reduced processing level. When processor load decreases, the system can increase processing reliability without incurring excessive delays, as the adaptation is responsive to actual system capacity.
Solution Approach 2:
The controller monitors processor load and provides feedback to adjust the number of processing engines applied to packet groups. This feedback mechanism ensures that processing reliability is maintained at appropriate levels based on actual processor availability, preventing both excessive delays and unnecessary reliability sacrifices.
4Productivity
If processing engines are dynamically adjusted based on load, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The controller serves multiple functions: it designates processing levels to packet groups, monitors processor load, measures system performance, and filters signals. This multi-functionality consolidates the complexity into a single control entity rather than requiring separate mechanisms for each function, making the dynamic adjustment system more manageable.
Solution Approach 2:
The system uses its own processing signals and performance data to make automatic adjustments to processing levels. The controller self-regulates the number of processing engines based on measured load conditions without requiring external intervention, reducing the operational complexity of managing dynamic resource allocation.
Data Source
AI summary
A Dynamic Adaptive Machine (DAM) classifies a received packet into a packet group and selects an initial level of processing for that group based on, at least in part, a measure of load of the system for processing the packet. The DAM continues to monitor the system load and re-computes the load measure, and increases or decreases the processing level according to the load measure. The DAM considers one or more processing states and, after an increase in the processing level, ignores for a certain grace period any errors generated at the higher level of processing, to minimize false positive errors.