Network Node Port Statistics Encoding for Short-Duration Load Detection
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Solution Overview
Problem
Existing solutions have limited ability to timely collect relevant data/statistics related to highly time-varying load conditions in computer networks, often missing specific load conditions of short durations or generating inaccurate information.
Innovation Solution
Implementing techniques to iteratively encode operational statistics from network nodes, such as ingress and egress ports, into bit vectors with high precision, allowing for accurate mapping and real-time monitoring of network conditions, including bandwidth and congestion, to facilitate proactive network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing data collection approaches are used, then network monitoring is performed, but the ability to timely collect relevant data/statistics related to short-duration load conditions is limited
Solution Approach 1:
The patent segments the network monitoring function into distributed network nodes that independently collect and encode operational statistics. Each node processes local data through iterative encoding algorithms, enabling parallel processing across multiple nodes. This segmentation allows the system to capture short-duration load conditions that would be missed by centralized or sequential approaches, simultaneously improving detection accuracy and reducing response time.
2Reliability
If traditional operational statistics collection methods are used, then general network monitoring is achieved, but specific load conditions of relatively short durations are missed or inaccurate information is generated
Solution Approach 1:
The patent implements dynamic iterative encoding algorithms that adapt to varying network conditions. The encoding process dynamically adjusts based on observed load patterns, allowing the system to maintain high accuracy for both short-duration and sustained load conditions. This dynamic approach ensures reliable detection across diverse traffic scenarios while comprehensively covering all load conditions that occur during the monitoring period.
3Measurement precision
If comprehensive network monitoring is implemented, then all load conditions are captured, but the complexity of data processing and encoding increases
Solution Approach 1:
The patent transforms operational statistics through parameter changes during the iterative encoding process. Raw network data is converted into encoded representations that preserve essential characteristics while reducing complexity. The encoding algorithm modifies data parameters systematically, achieving high-precision monitoring with manageable computational complexity through mathematical transformations rather than direct processing of raw data.
Data Source
AI summary
A pre-scaled accumulated byte count of a port of a network node over a sampling period is scaled with a scaling factor to generate a scaled accumulated byte count. The pre-scaled accumulated byte count represents a total number of bytes in packets transferred by the port. The scaling factor represents a first port-specific attribute of the port and scales a port-specific maximum throughput of the port to a specific maximum port throughput of the network node. An iterative vector encoding method is applied to the scaled accumulated byte count to generate an encoded bit vector comprising bits respectively ordered bit positions. Each set bit of the encoded bit vector represents a respective weighted value of port utilization of the port. The encoded bit vector is stored, at a map location, in an operational statistics map.