Multi-Level Outlier Detection for Server Load Balancing
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Solution Overview
Problem
Existing load balancing systems fail to detect and mitigate uneven distribution of loads among backend servers, leading to increased latency and poor user experience due to malfunctions or misconfigurations, which existing technologies are unable to effectively address.
Innovation Solution
A system employing multi-level outlier detection algorithms to identify anomalies in server loads and correlate them with potential causes, using statistical techniques like Box Plot methods to adjust load balancers and ensure even distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a load balancer distributes client requests among backend servers using traditional load balancing techniques, then the load is distributed among servers, but uneven load distribution occurs causing increased latency and poor user experience due to server malfunctions or misconfigurations that cannot be detected
Solution Approach 1:
The patent implements feedback by continuously monitoring server response times and load metrics, detecting anomalies when servers exhibit unusual behavior patterns. The system feeds this information back to the load balancer to dynamically adjust traffic distribution, preventing uneven load from causing increased latency. This closed-loop feedback mechanism resolves the contradiction by making load balancing reliable while preventing time loss through proactive anomaly detection and correction.
Solution Approach 2:
The patent introduces an intermediary anomaly detection system between the load balancer and backend servers. This intermediary monitors server health and load conditions, identifying malfunctions or misconfigurations before they cause significant latency issues. By acting as a mediator that filters and analyzes server performance data, the system prevents problematic traffic routing, thereby maintaining both load balancing reliability and minimizing server delay.
2Device complexity
If traditional load balancing techniques are used without anomaly detection, then the system structure remains simple, but uneven load distribution among servers cannot be detected or mitigated leading to increased processing delay
Solution Approach 1:
The patent replaces traditional mechanical load balancing mechanisms with an intelligent anomaly detection system that uses statistical analysis and machine learning. Instead of relying on simple round-robin or weight-based distribution, the system substitutes advanced analytics to identify patterns of uneven load distribution. This substitution maintains relatively simple system architecture while dramatically improving server processing efficiency through intelligent anomaly detection and adaptive load routing.
Solution Approach 2:
The patent implements self-service by enabling the load balancing system to automatically detect and correct uneven load distribution without manual intervention. The anomaly detection system continuously monitors server performance, identifies imbalances caused by malfunctions or misconfigurations, and automatically adjusts traffic distribution. This self-correcting mechanism keeps system complexity low while maintaining high server processing efficiency through automated anomaly mitigation.
3Measurement precision
If multi-level outlier detection algorithms are implemented to detect uneven load balancing, then anomalies in server loads can be identified and mitigated, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the anomaly detection process into multiple hierarchical levels. Instead of using a single complex detection algorithm, the system segments detection into different tiers: basic anomaly detection at the server level, intermediate pattern recognition at the load balancer level, and advanced statistical analysis at the system level. This segmentation achieves high measurement precision for detecting uneven load balancing while keeping each individual detection component relatively simple and manageable.
Data Source
AI summary
The present disclosure is directed towards systems and methods of detecting a cause of anomalous load balancing among a plurality of servers. A device intermediary to a plurality of clients and a plurality of servers collects values of a plurality of counters. The device identifies a server of the plurality of servers that is an outlier. The device can identify a counter of the plurality of counters that is an outlier based on at least a comparison of values of each of the plurality of counters for each of the plurality of servers. The device can provide, responsive to the determination, an indication that a value of the counter is a factor causing the server to have uneven load balancing during the time interval.