Adaptive Network Compression for Latency Control
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Solution Overview
Problem
Conventional network switches lack adaptive data compression techniques that adjust to changing network congestion levels, potentially increasing latency in both congested and uncongested networks.
Innovation Solution
Implementing an adaptive compression technique in network devices that responds to congestion indications by adjusting compression parameters such as string matching, hash table memory, and entropy encoding, enabling dynamic compression ratios based on quantized congestion levels to optimize end-to-end latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data compression is applied in network switches, then network congestion is reduced and end-to-end latency is lowered in congested networks, but latency increases in uncongested networks
Solution Approach 1:
The patent implements dynamic compression by enabling the compression engine to be selectively activated or deactivated based on real-time congestion indicators from the network. The system transitions between compressed and uncompressed transmission modes according to congestion conditions, making the compression capability dynamic rather than static.
Solution Approach 2:
The system changes the compression parameter (enabled/disabled state) based on congestion level indicators. When congestion is detected, the compression engine is enabled to reduce packet size and latency; when uncongested, compression is disabled to avoid processing overhead and potential latency increases.
2Productivity
If compression parameters are adjusted based on quantized congestion levels, then compression ratio is optimized for different congestion scenarios, but device complexity increases
Solution Approach 1:
The system uses congestion indicators as feedback from the network to automatically adjust compression parameters. The quantized congestion levels provide feedback signals that trigger corresponding compression ratio adjustments, creating a closed-loop control system that optimizes compression without manual intervention.
Solution Approach 2:
The compression engine dynamically adjusts its compression ratio based on real-time congestion feedback. Different compression levels are applied according to the severity of congestion, allowing the system to optimize productivity under varying network conditions without requiring complex manual configuration.
3Ease of operation
If simple pause notifications are used for congestion indication, then implementation is simple, but control over compression is coarse-grained
Solution Approach 1:
The system changes from binary pause notifications to multi-level quantized congestion indicators. This allows the compression engine to select from multiple compression ratios based on the specific congestion level, providing fine-grained control while maintaining relatively simple implementation through standardized indicator formats.
Data Source
AI summary
A network device is adaptively configured to compress an output data stream, responsive to congestion in the network. The network device receives indications of network congestion from another network device. Upon receipt of a congestion indication, the network device can adapt the compression technique to attempt to achieve more or less compression, depending on whether the congestion indication indicates more or less congestion. By adapting the compression to the level of network congestion, end-to-end latency of the network can potentially be decreased.