Cloud Server Scaling for Market Data Distribution

Solution Overview

Problem

Financial organizations face challenges in migrating Thomson Reuters Enterprise Platform (TREP) to a public cloud computing environment due to limitations in handling large volumes of data and scalability, necessitating an alternative hosting solution that is broadly accessible.

Innovation Solution

A method and system for hosting a market data distribution platform in a public cloud environment, utilizing a processor to dynamically adjust the number of cloud servers based on demand, with continuous monitoring and load shifting between servers, and employing a multicast communication protocol for efficient data distribution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If TREP is hosted on traditional infrastructure, then data handling capability is sufficient, but scalability and modern computing infrastructure compatibility are limited

Engineering Contradiction:

Improvecompatibility with modern computing infrastructureVSAvoiddata handling capability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system segments the TREP platform into multiple cloud servers that can independently handle data distribution tasks. This allows the platform to maintain reliability through distributed data handling while achieving scalability by adding or removing servers based on demand, thus resolving the contradiction between traditional infrastructure reliability and modern cloud scalability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts the number of active cloud servers based on real-time data distribution demands. During peak periods, additional servers are activated to handle increased load, while during off-peak periods, servers are reduced to optimize costs. This dynamic adaptation enables both reliable data handling and infrastructure scalability.

Inventive Principle:

Principle #15Dynamics

2Reliability

If cloud servers are increased to handle peak demand, then service availability is improved, but cost and resource utilization efficiency deteriorate

Engineering Contradiction:

Improveservice availabilityVSAvoidnumber of cloud servers

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts the number of active cloud servers based on real-time data distribution demands. During peak periods, additional servers are activated to handle increased load, while during off-peak periods, servers are reduced to optimize costs. This dynamic adaptation enables both reliable data handling and infrastructure scalability.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system continuously monitors data distribution metrics and uses this feedback to determine the optimal number of servers required. By implementing a feedback loop that adjusts server capacity based on actual demand patterns, the system maintains service availability during peak periods while minimizing server quantities during normal periods, thus resolving the contradiction between reliability and resource quantity.

Inventive Principle:

Principle #23Feedback

3Device complexity

If fixed number of cloud servers is used, then system simplicity is maintained, but adaptability to demand fluctuations deteriorates

Engineering Contradiction:

Improvesystem simplicityVSAvoidresponse to demand fluctuations

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system implements self-service automation where the platform automatically monitors its own performance metrics and adjusts server capacity without human intervention. The automated scaling mechanism adds or removes servers based on predefined thresholds and demand patterns, maintaining system simplicity while achieving high adaptability to demand fluctuations.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts the number of active cloud servers based on real-time data distribution demands. During peak periods, additional servers are activated to handle increased load, while during off-peak periods, servers are reduced to optimize costs. This dynamic adaptation enables both reliable data handling and infrastructure scalability.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12197957B2Method and system for hosting platform in public cloud computing environment

Publication Date: 2025.01.14 JPMORGAN CHASE BANK NA

AI summary

A method and a system for hosting a market data distribution platform that is widely used by many financial organizations, such as Thomson Reuters Enterprise Platform (TREP), in a public cloud computing environment is provided. The method includes facilitating a communication between the market data distribution platform and at least one server that operates in the public cloud; monitoring an amount of data being distributed on the platform and a number of clients that are accessing the platform; determining a minimum number of cloud servers required for servicing the clients; accessing at least the determined minimum number of servers; and facilitating communications between the servers and the platform. The number of servers and/or load amounts being handled by each server may be adjusted based on varying amounts of data and/or varying numbers of clients.