Edge Server Market Data Filtering for Latency Reduction
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Solution Overview
Problem
Current market data processing systems face significant latency and delays due to the high volume of unanalyzed, aggregated data being transmitted from multiple liquidity destinations to centralized servers, leading to queuing issues and loss of data, which hinders timely trading decisions.
Innovation Solution
A distributed system with edge servers located near liquidity destinations for real-time data collection and normalization, allowing for user-defined market condition monitoring and notification, reducing the need for extensive data transmission and enabling prompt action based on market conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If market data is aggregated and transmitted from multiple liquidity destinations to centralized servers, then data consolidation is achieved, but latency and processing delays increase due to high volume of unanalyzed data
Solution Approach 1:
The patent applies preliminary action by performing data normalization and filtering at the edge servers before data is transmitted to centralized servers. This preprocessing reduces the volume of data that needs to be transmitted and processed centrally, thereby reducing latency while achieving data consolidation. The edge servers normalize market data from multiple liquidity destinations and filter it according to user criteria before transmission.
Solution Approach 2:
The patent segments the data processing function by distributing edge servers at multiple liquidity destinations rather than relying on a single centralized processing point. Each edge server independently normalizes and filters data from its local liquidity destination, dividing the overall processing load and reducing the time for data to travel and be processed centrally.
2Loss of information
If high volume of market data is transmitted to centralized servers, then complete market information is available, but bandwidth requirements and processing load increase
Solution Approach 1:
The patent extracts only the relevant market data that matches user-defined criteria at the edge servers, rather than transmitting all market data to centralized servers. This extraction process filters out unnecessary information locally, reducing bandwidth consumption while ensuring that complete and accurate relevant market information is still available for trading decisions.
Solution Approach 2:
The patent implements local quality by having each edge server perform data normalization and filtering tailored to local market conditions and user-specific criteria at each liquidity destination. This localized processing ensures that data is optimized for its specific context before transmission, reducing overall bandwidth requirements while maintaining information quality.
3Reliability
If data is queued for processing at centralized servers, then all data can be processed systematically, but additional latency is introduced and data may be dropped when queues reach maximum capacity
Solution Approach 1:
The patent applies preliminary action by normalizing and filtering market data at edge servers before transmission to centralized servers. This preprocessing reduces the volume of data requiring queueing at centralized servers, minimizing queueing delays and reducing the risk of data drops while maintaining systematic processing reliability for the reduced data volume.
4Loss of time
If edge servers preprocess and filter market data locally, then latency is reduced and bandwidth requirements decrease, but system complexity increases
Solution Approach 1:
The patent applies universality by designing edge servers that perform multiple functions: data collection, normalization, filtering, and local analysis. This multi-functionality consolidates what could be separate complex systems into unified edge servers, reducing overall system complexity while achieving latency reduction and bandwidth optimization through local preprocessing.
Data Source
AI summary
Methods and systems for monitoring market data are disclosed. Real time data is collected that is related to conditions of a trading market. Collection occurs at an edge server associated with a liquidity destination trading at least one financial article of trade. The real time data that is collected can also be normalized if desired into a standard form. A user defined criteria is received from a centralized hub. The user defined criteria defines a particular event in the condition. It is then determined when a condition in the trading market matches the event. A response is generated providing notification of the occurrence of the event. The response is sent to the centralized hub for distribution to a user associated with the user defined criteria.