Server Traffic Rebalancing with Coefficient of Variation Detection
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Solution Overview
Problem
Existing traffic distribution methods fail to efficiently rebalance traffic across servers in real-time, particularly when traffic becomes concentrated on specific servers, leading to performance imbalances and potential connection issues.
Innovation Solution
A traffic distribution system that separates the managing system and load balancer into different environments, with the managing system calculating real-time coefficients of variation and standard deviations to detect imbalances, and the load balancer performing traffic distribution based on these calculations, using a server-specific weight table to optimize traffic distribution across servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex computation is performed in the same environment as traffic distribution, then detection accuracy improves, but resource efficiency deteriorates
Solution Approach 1:
The system divides the computing environment into two separate environments: a first environment for complex computation (managing system) and a second environment for traffic distribution (load balancer). This segmentation allows the managing system to perform resource-intensive coefficient of variation calculations without impacting the performance of the load balancer, while still achieving accurate traffic distribution detection.
Solution Approach 2:
The patent introduces an intermediary mechanism where the managing system in the first environment calculates traffic distribution metrics and transmits results to the load balancer in the second environment. This intermediary approach enables complex computations to be performed separately while maintaining accurate detection capabilities.
2Speed
If real-time calculation is performed continuously, then response speed improves, but resource consumption increases
Solution Approach 1:
The system performs real-time coefficient of variation calculations at periodic intervals rather than continuously. The managing system calculates the coefficient of variation for traffic data at regular time intervals, which maintains responsive detection capability while significantly reducing overall resource consumption compared to continuous calculation.
Solution Approach 2:
The managing system automatically performs resource-intensive calculations and transmits results to the load balancer, enabling the load balancer to make distribution decisions based on pre-computed data without performing heavy computations itself. This self-service approach optimizes resource usage across the system.
3Reliability
If traffic distribution is performed frequently, then load balancing improves, but connection stability deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the managing system monitors traffic distribution patterns and adjusts coefficient of variation calculations based on detected imbalances. The load balancer receives updated distribution metrics and adjusts traffic routing accordingly, creating a feedback loop that improves load balancing while maintaining connection stability through controlled adjustment frequency.
Data Source
AI summary
A traffic distribution method may include acquiring traffic data on a quantity of inbound traffic to each of a plurality of servers; calculating a coefficient of variation of the traffic data for a first unit time duration, an interval average of coefficients of variation for a second unit time duration, and an interval standard deviation of the coefficients of variation for the second unit time duration, wherein the second unit time duration includes a plurality of first unit time durations; performing a real-time calculation on the coefficient of variation of the traffic data for the first unit time duration to calculate a real-time coefficient of variation; determining whether a traffic distribution imbalance state across the plurality of servers occurs, based on comparing the real-time coefficient of variation with the interval average and the interval standard deviation; and performing traffic distribution across the plurality of servers, based on the determination.