ML Transaction Queueing for Ecommerce Stability
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Solution Overview
Problem
Ecommerce platforms face performance issues and revenue loss due to high demand and limited supply, leading to frustrated customers and lost sales, as existing systems are not equipped to handle sudden spikes in traffic and are not robust enough to maintain a high-quality user experience during peak periods.
Innovation Solution
A high-volume transaction queueing system using machine learning algorithms that monitors the health of the primary system, automatically queues orders, and throttles transaction submission rates to prevent overload, ensuring orders are not lost and maintaining a high-quality user experience by seamlessly transitioning between normal and spike traffic handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the ecommerce system infrastructure is scaled up to handle high traffic spikes, then the system can handle peak demand, but the infrastructure cost and complexity increase significantly
Solution Approach 1:
The patent introduces an intermediary queueing system that sits between the user interface and the primary ecommerce platform. This queueing system accepts transactions during peak demand and forwards them to the primary platform when it can handle the load, effectively mediating between high user demand and limited platform capacity without requiring the platform itself to be continuously oversized.
Solution Approach 2:
The system performs preliminary actions by pre-queuing transactions before the primary platform becomes overloaded. The queueing system monitors platform health metrics and proactively queues transactions when it detects approaching capacity limits, preventing the platform from being suddenly overwhelmed by spikes in demand.
2Reliability
If the system queues all transactions during peak load, then the primary platform is protected from overload, but transaction processing time increases
Solution Approach 1:
The system applies partial queuing action by selectively queuing only the portion of transactions that would cause overload, while allowing normal transactions to pass through directly. The queueing threshold is dynamically adjusted based on platform health metrics, ensuring that not all transactions are queued during peak load, but only enough to maintain platform stability.
Solution Approach 2:
The queueing system dynamically adjusts its behavior based on real-time monitoring of platform health. When the platform is healthy, transactions are processed directly with minimal queuing. When the platform approaches capacity limits, the system dynamically increases queuing to protect the platform, and when the platform recovers, it dynamically reduces queuing to minimize processing delays.
3Reliability
If the system monitors and queues transactions in real-time, then transaction loss is prevented, but system resource consumption increases
Solution Approach 1:
The queueing system implements self-service by automatically monitoring its own performance metrics and adjusting its queuing behavior without external intervention. It self-regulates based on platform health data, dynamically adjusting queue depth and transaction routing decisions to optimize both transaction capture and resource consumption.
Solution Approach 2:
The system uses feedback mechanisms where platform health metrics are continuously monitored and fed back into the queueing decision logic. This feedback loop allows the system to adjust its queuing behavior in real-time based on actual platform performance, optimizing the balance between transaction capture and resource usage.
Data Source
AI summary
Embodiments of the invention are directed to a system and method for providing a high-volume transaction queueing, reserve ecommerce solution that automatically engages and queues transactions when a primary back-end transaction processing system becomes unresponsive or unstable. Through machine learning algorithms, embodiments of the invention control transaction submission rates by queuing them and throttling the rate at which they are processed based on self-awareness and constant monitoring, feedback and health checks of the primary system. When metrics indicate that the third-party system can begin accepting transactions again, the system automatically feeds the queued transactions along with real-time orders at a rate that the third-party system can successfully manage.