Intelligent Link Load Balancing via Latency Prediction
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Solution Overview
Problem
Existing link load balancing techniques fail to predict upcoming network loads accurately, leading to unequal distribution of traffic and inefficient utilization of network service provider (NSP) links, as they do not adequately analyze factors like packet loss rates, traffic speeds, and high traffic conditions.
Innovation Solution
A method and system for intelligent link load balancing that monitors ongoing network traffic, predicts current latency levels for each NSP based on historical data, and determines the optimal route by analyzing the current network latency, utilizing a relationship learned from historical latency and traffic data to route traffic through the least utilized NSP.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If threshold based pre-defined method is employed for load balancing, then the link load balancer can operate with simple rules, but it fails to predict upcoming load resulting in unequal distribution of load
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network traffic transaction data and learning relationships between latency and traffic patterns before actual load balancing decisions are needed. This predictive monitoring enables the system to anticipate upcoming load conditions rather than merely reacting to current states, thereby improving load distribution efficiency while maintaining manageable complexity through automated learning.
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing historical network latency and traffic data to learn relationships that inform future routing decisions. This closed-loop feedback allows the link load balancer to adapt to changing network conditions dynamically, improving productivity through more accurate load prediction while the automated learning process keeps operational complexity reasonable.
2Ease of operation
If proximity detection technique is employed, then load balancing can be performed based on physical link proximity, but it does not analyze packet loss rates, traffic speeds, and high traffic conditions resulting in unutilized NSP links
Solution Approach 1:
The system performs preliminary monitoring and analysis of comprehensive network parameters including packet loss rates, traffic speeds, and latency patterns before making routing decisions. This advance analysis of multiple traffic conditions enables more accurate prediction of optimal NSP link utilization, ensuring that physical proximity alone does not dictate routing but is combined with real-time performance metrics to maximize link usage efficiency.
Solution Approach 2:
The system transitions from relying on a single parameter (physical proximity) to analyzing multiple dynamic parameters including packet loss rates, traffic speeds, latency levels, and bandwidth availability. This multi-parameter approach allows the system to adapt routing decisions based on actual network conditions, significantly improving NSP link utilization while maintaining ease of operation through automated multi-factor analysis.
3Productivity
If traditional traffic management methods are used with client and end-user round-trip times, then basic load balancing can be achieved, but upcoming load cannot be predicted resulting in less efficient link load balancer
Solution Approach 1:
The system performs preliminary learning and analysis of network traffic patterns and latency relationships before actual load balancing decisions are required. By continuously monitoring historical data and establishing predictive models in advance, the system can anticipate upcoming load conditions rather than merely reacting to current round-trip times, thereby reducing the time loss associated with prediction inaccuracies while maintaining efficient load balancing operations.
Solution Approach 2:
The system implements feedback loops that continuously analyze historical network latency and traffic transaction data to refine predictive models. This ongoing feedback mechanism allows the system to learn from past performance patterns and improve its ability to predict upcoming load conditions, reducing prediction errors and time losses while maintaining productive load balancing performance through adaptive learning.
Data Source
AI summary
This disclosure relates to method and system for intelligent link load balancing. In one embodiment, a method for performing intelligent link load balancing in a computer network including a number of network service providers (NSPs) is disclosed. The method includes monitoring ongoing network traffic transaction data of the computer network, predicting a current network latency level for the ongoing network traffic transaction data for each of the NSPs based on a relationship between a network latency level and network traffic transaction data for each of the NSPs, determining an optimal NSP to route ongoing network traffic based on an analysis of the current network latency level of each of the NSPs, and effecting routing of the ongoing network traffic through the optimal NSP. The relationship is learnt based on an analysis of historical network latency level and historical network traffic transaction data for each of the NSPs.