Latency Prediction for Network Message Microtiming
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Solution Overview
Problem
Networks face challenges in anticipating and adapting to latency spikes, leading to unnecessary data transmissions and congestion, particularly in high-frequency trading and currency arbitrage systems where reliability and low latency are critical.
Innovation Solution
A system with a central routing device that continuously updates a probabilistic future latency model using utilization and timing data from mesh networks, delaying packet transmission when expected latency exceeds a threshold to prevent congestion and ensure data freshness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If TCP/IP waits for confirmation within a fixed time and re-transmits on failure, then reliability is improved, but network congestion increases due to massive re-transmissions when latency is high
Solution Approach 1:
The system performs preliminary latency prediction using historical timing data and utilization metrics before transmitting messages. By anticipating latency conditions in advance, the system can proactively adjust transmission timing and confirmation windows, preventing the need for massive re-transmissions while maintaining reliable delivery
Solution Approach 2:
The confirmation time window and transmission timing are made dynamic rather than fixed. The system continuously adapts these parameters based on real-time network conditions and predicted latency, allowing the protocol to optimize between reliability and throughput by adjusting behavior to current network state
2Productivity
If messages are transmitted during high latency periods, then data throughput is maintained, but the data becomes obsolete by the time it is received
Solution Approach 1:
The system predicts future latency conditions before initiating transmissions. By analyzing historical timing data and current utilization metrics, it anticipates when latency will exceed thresholds that would make data obsolete, and adjusts transmission timing accordingly to ensure data remains fresh upon receipt
Solution Approach 2:
The system continuously monitors actual message timing and latency feedback, comparing predicted versus actual values to refine its latency model. This feedback loop enables the system to learn from past transmissions and improve its ability to predict when data would become obsolete, adjusting future transmission decisions accordingly
3Reliability
If confirmation windows are extended to accommodate high latency, then reliability is improved, but transmission delays increase and congestion worsens
Solution Approach 1:
The system predicts latency conditions before transmission and sets appropriate confirmation windows in advance. Rather than using a fixed extended window that always causes delays, or a short window that risks missing confirmations, the system proactively configures the confirmation window size based on predicted latency, achieving reliable delivery without unnecessary delays
Solution Approach 2:
The confirmation window duration is changed as a variable parameter rather than a fixed value. The system dynamically adjusts this parameter based on predicted network conditions, using shorter windows when latency is low and longer windows when latency is expected to be high, thereby optimizing the balance between reliability and transmission delay
Data Source
AI summary
A system for network message microtiming, comprising a central routing device, a first mesh network, and a second mesh network. The central routing device continuously receives utilization data from one or more devices of the first mesh network; continuously receives timing data from one of more devices of the second mesh network; builds and continuously updates a probabilistic future latency model based at least in part on the utilization data and the timing data; receives a message from a client computing device comprising data to be sent to at least one of a plurality of remote computing devices via the first mesh network; determines that a current expected latency according to the future latency model exceeds a predetermined threshold; and as a result delays transmission of a packet comprising the data to be sent to at the least one of the plurality of remote computing devices.